Spurious correlation #3,250 · 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 statisticians in Maine and the second variable is Renewable energy production in U.S. Virgin Islands. The chart goes from 2011 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 statisticians in Maine and the second variable is Renewable energy production in U.S. Virgin Islands. The chart goes from 2011 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of statisticians in Maine grew, so did their love for data analysis. This led to the 'Maine Statistician Effect,' where they discovered the shocking correlation between strong oceanic winds in U.S. Virgin Islands and the potential for wind energy. It turns out, they really knew how to *blow* those renewable energy production numbers through the roof!

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Generate an image using the style of [ High-tech ] that shows a bright, sunny field in Maine. In the field, a group of statisticians are working amongst tall grass and wildflowers, their laptops and calculators at the ready. In the distance, a modern factory is churning out an increasing number of sleek, high-tech pocket protectors. As the statisticians work, the factory's production grows. In the U.S. Virgin Islands, mini solar panels are being installed, glistening in the sun, powered by the surplus energy produced in Maine. The panels stand out against the backdrop of the bright blue ocean, symbolizing the growth of renewable energy thanks to the efforts of the statisticians. 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 statisticians in Maine and Renewable energy production in U.S. Virgin Islands. 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: Cutsy art illustration Voxel art Psychedelic Realistic High-tech
ChatGPT: High-tech

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 statisticians in Maine and Renewable energy production in U.S. Virgin Islands, in the following situation: As the number of statisticians in Maine increased, so did the number of pocket protectors being manufactured, leading to a surplus of tiny solar panels. These mini solar panels were then shipped to the U.S. Virgin Islands and used to power renewable energy production, all thanks to the statisticians' love for data and renewable energy.. Keep the description simple.
ChatGPT: In a sunny field in Maine, a group of statisticians is busy at work, crunching numbers. As their ranks grow, so does the production of pocket protectors at a nearby factory. Meanwhile, in the U.S. Virgin Islands, mini solar panels are being installed, powered by the surplus energy produced in Maine. The bright panels stand out against the backdrop of blue ocean, symbolizing the renewable energy now flourishing thanks to the statisticians' efforts.

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: [ High-tech ] and depicting this scene: In a sunny field in Maine, a group of statisticians is busy at work, crunching numbers. As their ranks grow, so does the production of pocket protectors at a nearby factory. Meanwhile, in the U.S. Virgin Islands, mini solar panels are being installed, powered by the surplus energy produced in Maine. The bright panels stand out against the backdrop of blue ocean, symbolizing the renewable energy now flourishing thanks to the statisticians' efforts. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image using the style of [ High-tech ] that shows a bright, sunny field in Maine. In the field, a group of statisticians are working amongst tall grass and wildflowers, their laptops and calculators at the ready. In the distance, a modern factory is churning out an increasing number of sleek, high-tech pocket protectors. As the statisticians work, the factory's production grows. In the U.S. Virgin Islands, mini solar panels are being installed, glistening in the sun, powered by the surplus energy produced in Maine. The panels stand out against the backdrop of the bright blue ocean, symbolizing the growth of renewable energy thanks to the efforts of the statisticians.

*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 The number of statisticians in Maine caused Renewable energy production in U.S. Virgin Islands to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

The Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands

The Journal of Eccentric Statistical Analysis

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of statisticians in Maine and Renewable energy production in U.S. Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
The Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands

[[ABSTRACT]]
The present research aims to investigate the intriguing relationship between the number of statisticians employed in the state of Maine and the production of renewable energy in the U.S. Virgin Islands. Utilizing data sourced from the Bureau of Labor Statistics and the Energy Information Administration, our study delves into this unexplored correlation, seeking to shed light on the interconnectedness of these seemingly disparate elements.

While the study initially appeared to be an exercise in whimsy, the results have yielded a notable correlation coefficient of 0.8845634 and a remarkable p-value of less than 0.01 for the period spanning from 2011 to 2021. This statistical outcome has, quite aptly, electrified our research team – and not just due to the renewable energy context.

A lighthearted investigative endeavor, our findings offer a surprising twist in the realm of statistical research, providing a spark of curiosity for future inquiries. As the saying goes, "When you're feeling low, just go to your nearest renewable energy source... it'll give you a boost!"

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The link between the number of statisticians in the state of Maine and the production of renewable energy in the U.S. Virgin Islands may seem as incongruous as a polar bear in the Sahara. However, it is precisely this unusual juxtaposition that has sparked our curiosity and led us to embark on this seemingly whimsical, yet surprisingly illuminating, research endeavor.
"Dad joke incoming: Why did the statistician go to Maine? Because they heard there were some 'mean' renewable energy statistics to crunch there!"
As we delve into this uncharted territory, it is important to note that statistics play a pivotal role in shaping policy decisions, evaluating trends, and identifying potential areas for growth. And what better area for growth to explore than the renewable energy sector? Our investigation seeks to unravel the potential influence of statisticians on the nuts and bolts— or should we say ohms and volts— of renewable energy production.
"A little statistical humor: Never trust a statistician who calculates in the dark, they always love to shed light on their findings!"
As we delve into the murky depths of data, it becomes increasingly clear that the interconnectedness of seemingly unrelated factors often holds surprises. This research aims to uncover these surprises, much like opening a box of statistical fortune cookies – except instead of receiving obscure predictions, we will be gaining insight into the economic and environmental symbiosis between Maine and the U.S. Virgin Islands.
"Here's a statistical dad joke for you: Studying the correlation between statisticians in Maine and renewable energy production is like trying to find the X-intercept of a parabola – unexpected, but definitely worth the effort!"
In our quest to shed light on this unexplored correlation, we initially encountered skepticism from our peers. However, our initial exploration yielded striking preliminary findings that have left our research team charged with enthusiasm. The statistical wizardry at play has manifested a correlation coefficient that could power a small village and a p-value that has garnered more attention than a celebrity sighting.
"Dad joke alert: Why did the statistician bring a ladder to the renewable energy plant? Because the p-value was through the roof!"
So, with great enthusiasm, we present our findings – a fusion of statistical intrigue and renewable energy potential. This study aims to provide a current of thought-provoking insight into the interconnected world of statistics and sustainable energy. As we unravel the mysteries of this unexpected correlation, we invite fellow researchers to join us in this statistical schmooze, where even the most improbable relationships may hold the key to a brighter and greener future.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The authors endeavor to shed light on the curious relationship between the number of statisticians in the state of Maine and the production of renewable energy in the U.S. Virgin Islands. Previous studies by Smith et al. (2018) and Doe (2019) have provided foundational insights into the statistical landscape of Maine and the renewable energy potential of the U.S. Virgin Islands. The intersection of these two realms remains largely unexplored, prompting the present investigation.

In "Economics of Renewable Energy" by Brown (2017), the author discusses the economic implications of renewable energy production, highlighting its potential to reduce carbon emissions and contribute to sustainable development. Similarly, Jones (2016) explores the statistical methodologies employed in the evaluation of renewable energy initiatives. These sources, while not directly addressing the specific correlation under examination, offer valuable context for understanding the broader implications of renewable energy production.

Turning to fictional accounts, the novel "Maine" by Sullivan (2011) paints a vivid portrait of life in the state, capturing the essence of its rural landscapes and vibrant communities. In a similarly evocative vein, "Island of the Blue Dolphins" by O'Dell (1960) immerses readers in the natural beauty of an island setting, albeit one unburdened by the statistical inquisitions and renewable energy musings of our inquiry.

An unexpected but relevant meme relating to renewable energy is the "Solar Panel Meme," featuring various humorous depictions of solar panels in everyday situations. While the meme's primary intent may be entertainment, its presence in popular culture reflects a growing awareness of renewable energy sources and their potential for widespread integration.

Returning to the realm of serious inquiry, the present study sets out to unearth the hidden ties between the number of statisticians in Maine and the production of renewable energy in the U.S. Virgin Islands. The journey promises to be as illuminating as it is unexpected, much like a statistical surprise party - except instead of party favors, we anticipate uncovering insights that could power a small city.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of statisticians in Maine and Renewable energy production in U.S. Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and Energy Information Administration . You used data from 2011 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 Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands

[[ABSTRACT]]
The present research aims to investigate the intriguing relationship between the number of statisticians employed in the state of Maine and the production of renewable energy in the U.S. Virgin Islands. Utilizing data sourced from the Bureau of Labor Statistics and the Energy Information Administration, our study delves into this unexplored correlation, seeking to shed light on the interconnectedness of these seemingly disparate elements.

While the study initially appeared to be an exercise in whimsy, the results have yielded a notable correlation coefficient of 0.8845634 and a remarkable p-value of less than 0.01 for the period spanning from 2011 to 2021. This statistical outcome has, quite aptly, electrified our research team – and not just due to the renewable energy context.

A lighthearted investigative endeavor, our findings offer a surprising twist in the realm of statistical research, providing a spark of curiosity for future inquiries. As the saying goes, "When you're feeling low, just go to your nearest renewable energy source... it'll give you a boost!"

[[INTRODUCTION]]
The link between the number of statisticians in the state of Maine and the production of renewable energy in the U.S. Virgin Islands may seem as incongruous as a polar bear in the Sahara. However, it is precisely this unusual juxtaposition that has sparked our curiosity and led us to embark on this seemingly whimsical, yet surprisingly illuminating, research endeavor.
"Dad joke incoming: Why did the statistician go to Maine? Because they heard there were some 'mean' renewable energy statistics to crunch there!"
As we delve into this uncharted territory, it is important to note that statistics play a pivotal role in shaping policy decisions, evaluating trends, and identifying potential areas for growth. And what better area for growth to explore than the renewable energy sector? Our investigation seeks to unravel the potential influence of statisticians on the nuts and bolts— or should we say ohms and volts— of renewable energy production.
"A little statistical humor: Never trust a statistician who calculates in the dark, they always love to shed light on their findings!"
As we delve into the murky depths of data, it becomes increasingly clear that the interconnectedness of seemingly unrelated factors often holds surprises. This research aims to uncover these surprises, much like opening a box of statistical fortune cookies – except instead of receiving obscure predictions, we will be gaining insight into the economic and environmental symbiosis between Maine and the U.S. Virgin Islands.
"Here's a statistical dad joke for you: Studying the correlation between statisticians in Maine and renewable energy production is like trying to find the X-intercept of a parabola – unexpected, but definitely worth the effort!"
In our quest to shed light on this unexplored correlation, we initially encountered skepticism from our peers. However, our initial exploration yielded striking preliminary findings that have left our research team charged with enthusiasm. The statistical wizardry at play has manifested a correlation coefficient that could power a small village and a p-value that has garnered more attention than a celebrity sighting.
"Dad joke alert: Why did the statistician bring a ladder to the renewable energy plant? Because the p-value was through the roof!"
So, with great enthusiasm, we present our findings – a fusion of statistical intrigue and renewable energy potential. This study aims to provide a current of thought-provoking insight into the interconnected world of statistics and sustainable energy. As we unravel the mysteries of this unexpected correlation, we invite fellow researchers to join us in this statistical schmooze, where even the most improbable relationships may hold the key to a brighter and greener future.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study involved a series of convoluted and slightly whimsical approaches, befitting the unexpected nature of the research inquiry. First, data regarding the number of statisticians in the state of Maine was obtained from the Bureau of Labor Statistics, utilizing a technique lovingly referred to as "statistical fishing" due to the meticulous and patient collection process. This involved trawling through labor market surveys, employment trends, and occupational projections, not dissimilar to a statistical angler patiently waiting for the right catch.

"Dad joke time: Why did the statistician go fishing? To catch some outliers! They always like to reel in interesting data."

Simultaneously, data pertaining to renewable energy production in the U.S. Virgin Islands was obtained from the Energy Information Administration, utilizing a method humorously dubbed "data spelunking" due to the journey through cavernous databases in search of illuminating insights. This process involved mining energy production reports, consumption patterns, and renewable energy utilization statistics, akin to a data-driven explorer seeking treasure in the depths of information caverns.

As with any rigorous statistical inquiry, the collected data was meticulously cleaned and preened, akin to preparing cats for a feline beauty pageant, to ensure accuracy and reliability. This involved the identification and rectification of missing data, outliers, and inconsistencies, employing techniques of imputation and Bayesian inference.

"Dad joke insertion: Did you hear about the statistician who got lost in the data cleaning process? They said they felt like they were herding cats!"

Subsequently, a series of sophisticated statistical analyses were performed to uncover the potential relationship between the number of statisticians in Maine and renewable energy production in the U.S. Virgin Islands. Utilizing an array of regression models, including linear, logistic, and time series regressions, the data was scrutinized for potential correlations, causations, and unexpected surprises.

In addition, due to the longitudinal nature of the study, a time-series analysis using autoregressive integrated moving average (ARIMA) models was employed to capture any temporal dynamics in the relationship between the variables. This involved forecasting future renewable energy production based on past statistical trends, much like predicting the next move in a game of renewable energy chess.

Finally, the statistical significance of the findings was rigorously tested using hypothesis testing, with an emphasis on the calculation of p-values and confidence intervals. This culminated in the extraction of a correlation coefficient and p-value, serving as the lynchpins of statistical inference and, inadvertently, the cause of much excitement within the research team.

In summary, the methodology employed in this study was a fusion of statistical fishing, data spelunking, feline data grooming, and renewable energy chess, all aimed at unraveling the enigmatic relationship between statisticians in Maine and the production of renewable energy in the U.S. Virgin Islands.

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

Please make up the results section of an academic research paper presenting the findings of the connection between The number of statisticians in Maine and Renewable energy production in U.S. Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

For the time period 2011 to 2021, you found a correlation 0.8845634, r-squared of 0.7824524, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
The Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands

[[ABSTRACT]]
The present research aims to investigate the intriguing relationship between the number of statisticians employed in the state of Maine and the production of renewable energy in the U.S. Virgin Islands. Utilizing data sourced from the Bureau of Labor Statistics and the Energy Information Administration, our study delves into this unexplored correlation, seeking to shed light on the interconnectedness of these seemingly disparate elements.

While the study initially appeared to be an exercise in whimsy, the results have yielded a notable correlation coefficient of 0.8845634 and a remarkable p-value of less than 0.01 for the period spanning from 2011 to 2021. This statistical outcome has, quite aptly, electrified our research team – and not just due to the renewable energy context.

A lighthearted investigative endeavor, our findings offer a surprising twist in the realm of statistical research, providing a spark of curiosity for future inquiries. As the saying goes, "When you're feeling low, just go to your nearest renewable energy source... it'll give you a boost!"

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The link between the number of statisticians in the state of Maine and the production of renewable energy in the U.S. Virgin Islands may seem as incongruous as a polar bear in the Sahara. However, it is precisely this unusual juxtaposition that has sparked our curiosity and led us to embark on this seemingly whimsical, yet surprisingly illuminating, research endeavor.
"Dad joke incoming: Why did the statistician go to Maine? Because they heard there were some 'mean' renewable energy statistics to crunch there!"
As we delve into this uncharted territory, it is important to note that statistics play a pivotal role in shaping policy decisions, evaluating trends, and identifying potential areas for growth. And what better area for growth to explore than the renewable energy sector? Our investigation seeks to unravel the potential influence of statisticians on the nuts and bolts— or should we say ohms and volts— of renewable energy production.
"A little statistical humor: Never trust a statistician who calculates in the dark, they always love to shed light on their findings!"
As we delve into the murky depths of data, it becomes increasingly clear that the interconnectedness of seemingly unrelated factors often holds surprises. This research aims to uncover these surprises, much like opening a box of statistical fortune cookies – except instead of receiving obscure predictions, we will be gaining insight into the economic and environmental symbiosis between Maine and the U.S. Virgin Islands.
"Here's a statistical dad joke for you: Studying the correlation between statisticians in Maine and renewable energy production is like trying to find the X-intercept of a parabola – unexpected, but definitely worth the effort!"
In our quest to shed light on this unexplored correlation, we initially encountered skepticism from our peers. However, our initial exploration yielded striking preliminary findings that have left our research team charged with enthusiasm. The statistical wizardry at play has manifested a correlation coefficient that could power a small village and a p-value that has garnered more attention than a celebrity sighting.
"Dad joke alert: Why did the statistician bring a ladder to the renewable energy plant? Because the p-value was through the roof!"
So, with great enthusiasm, we present our findings – a fusion of statistical intrigue and renewable energy potential. This study aims to provide a current of thought-provoking insight into the interconnected world of statistics and sustainable energy. As we unravel the mysteries of this unexpected correlation, we invite fellow researchers to join us in this statistical schmooze, where even the most improbable relationships may hold the key to a brighter and greener future.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The analysis unveiled a strong correlation between the number of statisticians in the state of Maine and renewable energy production in the U.S. Virgin Islands, with a correlation coefficient of 0.8845634. This finding suggests that there is a robust relationship between the two variables, akin to the symbiotic bond between a calculator and a statistician – they just work better together!

The coefficient of determination (r-squared) further supported this association, revealing that approximately 78.25% of the variability in renewable energy production in the U.S. Virgin Islands could be explained by the number of statisticians in Maine. This remarkable r-squared value speaks volumes about the influential role of statisticians in shaping the landscape of sustainable energy production.

The p-value of less than 0.01 added an exclamation point to these findings, signifying that the observed correlation was highly statistically significant. In statistical parlance, this p-value is as rare as a unicorn in the world of hypothesis testing – a delightful rarity indeed!

Our research team's tireless efforts culminate in the presentation of Fig. 1, which visually depicts the pronounced correlation between the number of statisticians in Maine and renewable energy production in the U.S. Virgin Islands. The scatterplot, much like a well-crafted joke, conveys a clear message: there is a definite connection between the two variables, and it's not just a statistical fluke!

In conclusion, our study has unveiled a compelling association between the employment of statisticians in Maine and the generation of renewable energy in the U.S. Virgin Islands. This unexpected correlation underscores the interconnectedness of seemingly unrelated elements and invites further exploration into the mechanisms through which statisticians may impact sustainable energy production. As we bask in the glow of these revelatory findings, one thing is crystal clear: the statistical schmooze between Maine and the U.S. Virgin Islands holds promise for a brighter, greener future.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of statisticians in Maine and Renewable energy production in U.S. Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands

[[ABSTRACT]]
The present research aims to investigate the intriguing relationship between the number of statisticians employed in the state of Maine and the production of renewable energy in the U.S. Virgin Islands. Utilizing data sourced from the Bureau of Labor Statistics and the Energy Information Administration, our study delves into this unexplored correlation, seeking to shed light on the interconnectedness of these seemingly disparate elements.

While the study initially appeared to be an exercise in whimsy, the results have yielded a notable correlation coefficient of 0.8845634 and a remarkable p-value of less than 0.01 for the period spanning from 2011 to 2021. This statistical outcome has, quite aptly, electrified our research team – and not just due to the renewable energy context.

A lighthearted investigative endeavor, our findings offer a surprising twist in the realm of statistical research, providing a spark of curiosity for future inquiries. As the saying goes, "When you're feeling low, just go to your nearest renewable energy source... it'll give you a boost!"

[[LITERATURE REVIEW]]
The authors endeavor to shed light on the curious relationship between the number of statisticians in the state of Maine and the production of renewable energy in the U.S. Virgin Islands. Previous studies by Smith et al. (2018) and Doe (2019) have provided foundational insights into the statistical landscape of Maine and the renewable energy potential of the U.S. Virgin Islands. The intersection of these two realms remains largely unexplored, prompting the present investigation.
In "Economics of Renewable Energy" by Brown (2017), the author discusses the economic implications of renewable energy production, highlighting its potential to reduce carbon emissions and contribute to sustainable development. Similarly, Jones (2016) explores the statistical methodologies employed in the evaluation of renewable energy initiatives. These sources, while not directly addressing the specific correlation under examination, offer valuable context for understanding the broader implications of renewable energy production.
Turning to fictional accounts, the novel "Maine" by Sullivan (2011) paints a vivid portrait of life in the state, capturing the essence of its rural landscapes and vibrant communities. In a similarly evocative vein, "Island of the Blue Dolphins" by O'Dell (1960) immerses readers in the natural beauty of an island setting, albeit one unburdened by the statistical inquisitions and renewable energy musings of our inquiry.
An unexpected but relevant meme relating to renewable energy is the "Solar Panel Meme," featuring various humorous depictions of solar panels in everyday situations. While the meme's primary intent may be entertainment, its presence in popular culture reflects a growing awareness of renewable energy sources and their potential for widespread integration.
Returning to the realm of serious inquiry, the present study sets out to unearth the hidden ties between the number of statisticians in Maine and the production of renewable energy in the U.S. Virgin Islands. The journey promises to be as illuminating as it is unexpected, much like a statistical surprise party - except instead of party favors, we anticipate uncovering insights that could power a small city.

[[RESULTS]]
The analysis unveiled a strong correlation between the number of statisticians in the state of Maine and renewable energy production in the U.S. Virgin Islands, with a correlation coefficient of 0.8845634. This finding suggests that there is a robust relationship between the two variables, akin to the symbiotic bond between a calculator and a statistician – they just work better together!
The coefficient of determination (r-squared) further supported this association, revealing that approximately 78.25% of the variability in renewable energy production in the U.S. Virgin Islands could be explained by the number of statisticians in Maine. This remarkable r-squared value speaks volumes about the influential role of statisticians in shaping the landscape of sustainable energy production.
The p-value of less than 0.01 added an exclamation point to these findings, signifying that the observed correlation was highly statistically significant. In statistical parlance, this p-value is as rare as a unicorn in the world of hypothesis testing – a delightful rarity indeed!
Our research team's tireless efforts culminate in the presentation of Fig. 1, which visually depicts the pronounced correlation between the number of statisticians in Maine and renewable energy production in the U.S. Virgin Islands. The scatterplot, much like a well-crafted joke, conveys a clear message: there is a definite connection between the two variables, and it's not just a statistical fluke!
In conclusion, our study has unveiled a compelling association between the employment of statisticians in Maine and the generation of renewable energy in the U.S. Virgin Islands. This unexpected correlation underscores the interconnectedness of seemingly unrelated elements and invites further exploration into the mechanisms through which statisticians may impact sustainable energy production. As we bask in the glow of these revelatory findings, one thing is crystal clear: the statistical schmooze between Maine and the U.S. Virgin Islands holds promise for a brighter, greener future.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The unearthing of a significant correlation between the number of statisticians in Maine and renewable energy production in the U.S. Virgin Islands has illuminated a heretofore overlooked interconnectedness in the realms of statistical workforce deployment and sustainable energy generation. This unexpected finding sheds light on the intricate dance of variables that influence the global pursuit of renewable energy, proving that when it comes to statistical prowess and sustainable energy, there is indeed strength in numbers – and statisticians! It appears that statisticians are not just number crunchers; they may hold the key to unlocking renewable energy potential in unexpected places, much like a dad's keychain reveals the door to endless dad jokes.

Our results have aligned with prior research by Smith et al. (2018) and Doe (2019), who provided early insights into the statistical landscape of Maine and the renewable energy potential of the U.S. Virgin Islands. While their work laid a solid foundation for our investigation, our study has illuminated a direct and potent linkage between the two disparate domains, reinforcing the notion that curious statistical inquiry can yield significant real-world implications. It seems that the statistical landscape of Maine stretches far beyond its rocky terrain and coastal beauty to influence renewable energy dynamics in the distant U.S. Virgin Islands as well – a statistical ripple effect, if you will!

Furthermore, the pronounced correlation coefficient and the remarkable p-value in our analysis affirm the robustness of the statistical link between the number of statisticians in Maine and renewable energy production in the U.S. Virgin Islands. These statistical parameters serve as the equivalent of a resounding punchline in the scientific community, leaving no room for doubt regarding the meaningful association uncovered in our investigation. It's as if the statistical gods themselves have delivered a well-timed punchline, leaving us with no choice but to acknowledge the compelling humor of this statistical twist.

Moreover, the coefficient of determination (r-squared) further bolsters the credibility of our findings, revealing that a substantial portion of the variability in renewable energy production in the U.S. Virgin Islands can be accounted for by the number of statisticians in Maine. In essence, statisticians are not merely observers of statistical phenomena; they are active participants in shaping the renewable energy landscape, much like comedians shaping the narrative of a stand-up show. The statistical stage is set, and the performers, in this case, are the statisticians guiding renewable energy production toward a brighter future – a future that could only be brighter if it were powered by solar panels!

In summary, our present study has uncovered a statistically robust relationship between the employment of statisticians in Maine and renewable energy generation in the U.S. Virgin Islands, shedding light on a previously unexplored statistical schmooze with global implications. This unexpected correlation between seemingly unrelated elements invites further examination and underscores the potential for statisticians to play a pivotal role in shaping sustainable energy dynamics. Like a well-crafted academic paper, this statistical revelation promises to spark further inquiry and, possibly, a few more renewable energy-related puns along the way.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of statisticians in Maine and Renewable energy production in U.S. Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands

[[ABSTRACT]]
The present research aims to investigate the intriguing relationship between the number of statisticians employed in the state of Maine and the production of renewable energy in the U.S. Virgin Islands. Utilizing data sourced from the Bureau of Labor Statistics and the Energy Information Administration, our study delves into this unexplored correlation, seeking to shed light on the interconnectedness of these seemingly disparate elements.

While the study initially appeared to be an exercise in whimsy, the results have yielded a notable correlation coefficient of 0.8845634 and a remarkable p-value of less than 0.01 for the period spanning from 2011 to 2021. This statistical outcome has, quite aptly, electrified our research team – and not just due to the renewable energy context.

A lighthearted investigative endeavor, our findings offer a surprising twist in the realm of statistical research, providing a spark of curiosity for future inquiries. As the saying goes, "When you're feeling low, just go to your nearest renewable energy source... it'll give you a boost!"

[[INTRDUCTION]]
The link between the number of statisticians in the state of Maine and the production of renewable energy in the U.S. Virgin Islands may seem as incongruous as a polar bear in the Sahara. However, it is precisely this unusual juxtaposition that has sparked our curiosity and led us to embark on this seemingly whimsical, yet surprisingly illuminating, research endeavor.
"Dad joke incoming: Why did the statistician go to Maine? Because they heard there were some 'mean' renewable energy statistics to crunch there!"
As we delve into this uncharted territory, it is important to note that statistics play a pivotal role in shaping policy decisions, evaluating trends, and identifying potential areas for growth. And what better area for growth to explore than the renewable energy sector? Our investigation seeks to unravel the potential influence of statisticians on the nuts and bolts— or should we say ohms and volts— of renewable energy production.
"A little statistical humor: Never trust a statistician who calculates in the dark, they always love to shed light on their findings!"
As we delve into the murky depths of data, it becomes increasingly clear that the interconnectedness of seemingly unrelated factors often holds surprises. This research aims to uncover these surprises, much like opening a box of statistical fortune cookies – except instead of receiving obscure predictions, we will be gaining insight into the economic and environmental symbiosis between Maine and the U.S. Virgin Islands.
"Here's a statistical dad joke for you: Studying the correlation between statisticians in Maine and renewable energy production is like trying to find the X-intercept of a parabola – unexpected, but definitely worth the effort!"
In our quest to shed light on this unexplored correlation, we initially encountered skepticism from our peers. However, our initial exploration yielded striking preliminary findings that have left our research team charged with enthusiasm. The statistical wizardry at play has manifested a correlation coefficient that could power a small village and a p-value that has garnered more attention than a celebrity sighting.
"Dad joke alert: Why did the statistician bring a ladder to the renewable energy plant? Because the p-value was through the roof!"
So, with great enthusiasm, we present our findings – a fusion of statistical intrigue and renewable energy potential. This study aims to provide a current of thought-provoking insight into the interconnected world of statistics and sustainable energy. As we unravel the mysteries of this unexpected correlation, we invite fellow researchers to join us in this statistical schmooze, where even the most improbable relationships may hold the key to a brighter and greener future.

[[RESULTS]]
The analysis unveiled a strong correlation between the number of statisticians in the state of Maine and renewable energy production in the U.S. Virgin Islands, with a correlation coefficient of 0.8845634. This finding suggests that there is a robust relationship between the two variables, akin to the symbiotic bond between a calculator and a statistician – they just work better together!
The coefficient of determination (r-squared) further supported this association, revealing that approximately 78.25% of the variability in renewable energy production in the U.S. Virgin Islands could be explained by the number of statisticians in Maine. This remarkable r-squared value speaks volumes about the influential role of statisticians in shaping the landscape of sustainable energy production.
The p-value of less than 0.01 added an exclamation point to these findings, signifying that the observed correlation was highly statistically significant. In statistical parlance, this p-value is as rare as a unicorn in the world of hypothesis testing – a delightful rarity indeed!
Our research team's tireless efforts culminate in the presentation of Fig. 1, which visually depicts the pronounced correlation between the number of statisticians in Maine and renewable energy production in the U.S. Virgin Islands. The scatterplot, much like a well-crafted joke, conveys a clear message: there is a definite connection between the two variables, and it's not just a statistical fluke!
In conclusion, our study has unveiled a compelling association between the employment of statisticians in Maine and the generation of renewable energy in the U.S. Virgin Islands. This unexpected correlation underscores the interconnectedness of seemingly unrelated elements and invites further exploration into the mechanisms through which statisticians may impact sustainable energy production. As we bask in the glow of these revelatory findings, one thing is crystal clear: the statistical schmooze between Maine and the U.S. Virgin Islands holds promise for a brighter, greener future.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the intertwining tale of statisticians in Maine and renewable energy production in the U.S. Virgin Islands has been an unexpected journey filled with statistical surprises. Our findings have illuminated a robust correlation between these seemingly disparate elements, akin to the harmonious convergence of a pie chart and a bar graph - they just complement each other so well!

Our research, much like a renewable energy source, has shed light on the heretofore unexplored relationship between these variables, revealing a correlation coefficient tantamount to a statistical lightning bolt. This notable correlation coefficient could power a statistical power plant – or at the very least, an abacus or two!

As we wrap up our analysis, it is clear that the statistical prowess of Maine's number crunchers holds sway over the renewable energy landscape in the U.S. Virgin Islands. This unexpected statistical alliance, reminiscent of a statistical bromance, encourages us to contemplate the broader implications of statisticians as catalysts for sustainable energy development. It seems that statisticians might just be the 'watt' to get the renewable energy sector 'moving' – pun intended!

In keeping with the spirit of statistical inquiry, we cannot resist the temptation to end with a lighthearted statistical dad joke: "Why did the renewable energy statistician break up with their partner? They just couldn't find the right 'coefficient' for their relationship!"

In light of our revelatory findings, it is evident that our research has furnished a compelling insight into the statistical fabric interweaving Maine and the U.S. Virgin Islands. At this juncture, we assert, with statistical confidence, that no further research is needed in this area. The unexpected correlation has been unveiled, and it is now time to pass the statistical torch on to new frontiers of inquiry.

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 Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Eccentric Statistical Analysis

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 Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Renewable Energy and Statistical Analysis (IRESA)

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 Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands with an abstract of The present research aims to investigate the intriguing relationship between the number of statisticians employed in the state of Maine and the production of renewable energy in the U.S. Virgin Islands. Utilizing data sourced from the Bureau of Labor Statistics and the Energy Information Administration, our study delves into this unexplored correlation, seeking to shed light on the interconnectedness of these seemingly disparate elements.
While the study initially appeared to be an exercise in whimsy, the results have yielded a notable correlation coefficient of 0.8845634 and a remarkable p-value of less than 0.01 for the period spanning from 2011 to 2021. This statistical outcome has, quite aptly, electrified our research team – and not just due to the renewable energy context.
A lighthearted investigative endeavor, our findings offer a surprising twist in the realm of statistical research, providing a spark of curiosity for future inquiries. As the saying goes, "When you're feeling low, just go to your nearest renewable energy source... it'll give you a boost!

ChatGPT:

statisticians, renewable energy production, Maine, U.S. Virgin Islands, Bureau of Labor Statistics, Energy Information Administration, correlation coefficient, p-value, statistical research, renewable energy sources

*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 statisticians in Maine
Detailed data title: BLS estimate of statisticians in Maine
Source: Bureau of Larbor Statistics
See what else correlates with The number of statisticians in Maine

Renewable energy production in U.S. Virgin Islands
Detailed data title: Total renewable energy production in U.S. Virgin Islands in billion kWh
Source: Energy Information Administration
See what else correlates with Renewable energy production in U.S. Virgin Islands

Correlation r = 0.8845634 (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 = 0.7824524 (Coefficient of determination)
This means 78.2% of the change in the one variable (i.e., Renewable energy production in U.S. Virgin Islands) is predictable based on the change in the other (i.e., The number of statisticians in Maine) over the 11 years from 2011 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0003.^Show 0.0002982558313495015000000000
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 0.88 in 0.03% of random cases. Said differently, if you correlated 3,353 random variables^Note Which I absolutely did.
with the same 10 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 10 because we have two variables measured over a period of 11 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.

[ 0.61, 0.97 ] 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.

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
The number of statisticians in Maine (Statisticians)	40	40	40	70	70	100	60	90	70	90	80
Renewable energy production in U.S. Virgin Islands (Billion kWh)	0.00069	0.000885	0.001248	0.007179	0.013274	0.01333	0.00724	0.014	0.014	0.014	0.01708

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.

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([40,40,40,70,70,100,60,90,70,90,80,])
array_2 = np.array([0.00069,0.000885,0.001248,0.007179,0.013274,0.01333,0.00724,0.014,0.014,0.014,0.01708,])
array_1_name = "The number of statisticians in Maine"
array_2_name = "Renewable energy production in U.S. Virgin Islands"

# 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
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The spurious research paper: The Statistical Schmooze: Statisticians in the State of Maine and their Influence on Renewable Energy Production in the U.S. Virgin Islands

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Correlation ID: 3250 · Black Variable ID: 8863 · Red Variable ID: 24055


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