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AI explanation
As more Republicans in Wisconsin started feeling the heat of the political race, it inadvertently led to a spike in demand for geothermal energy. It's like their passion for the candidate seeped into the ground, revving up the geothermal activity all the way in Costa Rica. I guess you could say there was a 'republican't stop the geothermal power surge! It's a race for the energy votes, and the heat is on!
Model: dalle-3
Prompt: [A photo-realistic image] In a bustling town in Wisconsin, a group of people are seen casting their votes for the Republican Presidential candidate amidst a backdrop of vibrant fall foliage. The crowd exudes a palpable sense of political fervor as individuals engage in animated discussions about the heated race. Simultaneously, in a lush valley in Costa Rica, steam rises dramatically from the earth as geothermal power is harnessed to meet the increasing energy demands. The resonant connection between the surge in Republican votes and the rise in geothermal power is depicted through a subtle current of energy that spans across the borders, visually linking the two seemingly disparate scenes. 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: Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica. 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: Photorealism A photo-realistic image Minimalism Cybernetic art Symbolism
ChatGPT: A photo-realistic image
Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica, in the following situation: As more Republicans in Wisconsin started feeling the heat of the political race, it inadvertently led to a spike in demand for geothermal energy. It's like their passion for the candidate seeped into the ground, revving up the geothermal activity all the way in Costa Rica. I guess you could say there was a 'republican't stop the geothermal power surge! It's a race for the energy votes, and the heat is on!. Keep the description simple.
ChatGPT: In a bustling town in Wisconsin, a group of people are seen casting their votes for the Republican Presidential candidate, while discussing the heated political race. Concurrently, in a lush valley in Costa Rica, steam rises from the ground as geothermal power is being harnessed to meet the increased demand. The scene illustrates the connection between the surge in Republican votes and the rise in geothermal power, as if the passion for the candidate has sparked a surge of energy that transcends borders.
Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A photo-realistic image ] and depicting this scene: In a bustling town in Wisconsin, a group of people are seen casting their votes for the Republican Presidential candidate, while discussing the heated political race. Concurrently, in a lush valley in Costa Rica, steam rises from the ground as geothermal power is being harnessed to meet the increased demand. The scene illustrates the connection between the surge in Republican votes and the rise in geothermal power, as if the passion for the candidate has sparked a surge of energy that transcends borders. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [A photo-realistic image] In a bustling town in Wisconsin, a group of people are seen casting their votes for the Republican Presidential candidate amidst a backdrop of vibrant fall foliage. The crowd exudes a palpable sense of political fervor as individuals engage in animated discussions about the heated race. Simultaneously, in a lush valley in Costa Rica, steam rises dramatically from the earth as geothermal power is harnessed to meet the increasing energy demands. The resonant connection between the surge in Republican votes and the rise in geothermal power is depicted through a subtle current of energy that spans across the borders, visually linking the two seemingly disparate scenes.
*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.
I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Votes for the Republican Presidential candidate in Wisconsin caused Geothermal power generated in Costa Rica to increase.
AI academic paper
 |
Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus The Journal of Political Geoscience Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.
Please draft the title and abstract of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Include a pun in the title.
Your research team used data from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.8458923 and p < 0.01 for 1992 to 2020.
Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)
[[TITLE]]
Lorem ipsum dolor sit
[[ABSTRACT]]
Lorem ipsum dolor sit
ChatGPT:
[[TITLE]]
"Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus"
[[ABSTRACT]]
This paper investigates the unusual confluence of events connecting the political climate in Wisconsin with the geothermal power generation in Costa Rica. Employing data from reputable sources such as the MIT Election Data and Science Lab and the Harvard Dataverse, and collaborating with the Energy Information Administration, our research team sought to unearth the relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. Surprisingly, our analysis revealed a significant correlation coefficient of 0.8458923 and p < 0.01 over the period from 1992 to 2020. This unexpected finding sparks the tantalizing question of whether the earth beneath us holds some cryptic influence on the political leanings of a distant populace. The results of this study highlight the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the introduction section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the introduction section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica. 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.
Here is the title and abstract of the paper:
[[TITLE]]
"Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus"
[[ABSTRACT]]
This paper investigates the unusual confluence of events connecting the political climate in Wisconsin with the geothermal power generation in Costa Rica. Employing data from reputable sources such as the MIT Election Data and Science Lab and the Harvard Dataverse, and collaborating with the Energy Information Administration, our research team sought to unearth the relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. Surprisingly, our analysis revealed a significant correlation coefficient of 0.8458923 and p < 0.01 over the period from 1992 to 2020. This unexpected finding sparks the tantalizing question of whether the earth beneath us holds some cryptic influence on the political leanings of a distant populace. The results of this study highlight the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
Introduction
The intersection of politics, geothermal power, and statistical analysis might seem like an unexpected trio, even for the savviest data scientist. However, as we delve into the enigmatic relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica, we find ourselves in an absolutely sizzling venture. Like a lively geothermal spring bubbling beneath the earth's surface, the statistical correlation uncovered in our research has left us both electrified and bemused.
As researchers, we are continuously drawn to the undulating landscapes of data, seeking to unravel the mysteries that lie dormant within. When this particular correlation first emerged from the depths of our statistical analysis, our initial reaction was akin to discovering a rare, hidden gemstone in an abandoned mine—both exhilarating and slightly absurd. And yet, there it was, flickering on our computer screens like a mischievous spark from a subterranean wellspring, challenging the conventional boundaries of cause and effect.
The idea that the votes cast in the heartland of American democracy could hold some tether to the geothermal forces pulsating in the heart of Central America seems, at first glance, as preposterous as a chicken trying to tap dance on a fault line. Yet, as we delved deeper into the labyrinthine complexities of the data, the evidence began to congeal like the molten magma within a volcanic chamber. The statistical relationship we unearthed, much like a dormant geyser roused to life, demands closer scrutiny and may harbor broader implications than the initial absurdity suggests.
Our investigation, conducted with rigor and unyielding curiosity, harnessed data from esteemed sources such as the MIT Election Data and Science Lab, the Harvard Dataverse, and the Energy Information Administration. The statistical rigor applied to these datasets was akin to combing through layers of rock to expose the elusive treasures hidden within. Our collaborative effort with these data sources has allowed us to shine a luminescent heat lamp on this captivating correlation and raise a toast to the tantalizing, albeit quirky, nature of scientific inquiry.
As we embark on this academic escapade, it is not lost on us that the marriage of political votes and geothermal energy might seem as odd a pair as a penguin at a desert oasis. But with the discovery of a statistically significant correlation coefficient of 0.8458923 and p < 0.01 spanning nearly three decades, the evidence speaks for itself in a resounding duet of rock-solid reliability and flustered bewilderment.
Next, we will meticulously dissect the intricate methodology employed in this study, offering a robust framework for understanding the statistical alchemy that has brought these two seemingly disparate variables into an unforeseen harmony. But before we descend into the depths of our statistical cauldron, it is prudent to reflect on the bewitching nature of the correlations we have uncovered and embrace the delightful idiosyncrasies that animate the often staid realm of academic research.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a literature review section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica. 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. Perhaps you also got inspiration from some board games that are vaugely related.
Here is the title and abstract of the paper:
[[TITLE]]
"Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus"
[[ABSTRACT]]
This paper investigates the unusual confluence of events connecting the political climate in Wisconsin with the geothermal power generation in Costa Rica. Employing data from reputable sources such as the MIT Election Data and Science Lab and the Harvard Dataverse, and collaborating with the Energy Information Administration, our research team sought to unearth the relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. Surprisingly, our analysis revealed a significant correlation coefficient of 0.8458923 and p < 0.01 over the period from 1992 to 2020. This unexpected finding sparks the tantalizing question of whether the earth beneath us holds some cryptic influence on the political leanings of a distant populace. The results of this study highlight the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Introduction
The intersection of politics, geothermal power, and statistical analysis might seem like an unexpected trio, even for the savviest data scientist. However, as we delve into the enigmatic relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica, we find ourselves in an absolutely sizzling venture. Like a lively geothermal spring bubbling beneath the earth's surface, the statistical correlation uncovered in our research has left us both electrified and bemused.
As researchers, we are continuously drawn to the undulating landscapes of data, seeking to unravel the mysteries that lie dormant within. When this particular correlation first emerged from the depths of our statistical analysis, our initial reaction was akin to discovering a rare, hidden gemstone in an abandoned mine—both exhilarating and slightly absurd. And yet, there it was, flickering on our computer screens like a mischievous spark from a subterranean wellspring, challenging the conventional boundaries of cause and effect.
The idea that the votes cast in the heartland of American democracy could hold some tether to the geothermal forces pulsating in the heart of Central America seems, at first glance, as preposterous as a chicken trying to tap dance on a fault line. Yet, as we delved deeper into the labyrinthine complexities of the data, the evidence began to congeal like the molten magma within a volcanic chamber. The statistical relationship we unearthed, much like a dormant geyser roused to life, demands closer scrutiny and may harbor broader implications than the initial absurdity suggests.
Our investigation, conducted with rigor and unyielding curiosity, harnessed data from esteemed sources such as the MIT Election Data and Science Lab, the Harvard Dataverse, and the Energy Information Administration. The statistical rigor applied to these datasets was akin to combing through layers of rock to expose the elusive treasures hidden within. Our collaborative effort with these data sources has allowed us to shine a luminescent heat lamp on this captivating correlation and raise a toast to the tantalizing, albeit quirky, nature of scientific inquiry.
As we embark on this academic escapade, it is not lost on us that the marriage of political votes and geothermal energy might seem as odd a pair as a penguin at a desert oasis. But with the discovery of a statistically significant correlation coefficient of 0.8458923 and p < 0.01 spanning nearly three decades, the evidence speaks for itself in a resounding duet of rock-solid reliability and flustered bewilderment.
Next, we will meticulously dissect the intricate methodology employed in this study, offering a robust framework for understanding the statistical alchemy that has brought these two seemingly disparate variables into an unforeseen harmony. But before we descend into the depths of our statistical cauldron, it is prudent to reflect on the bewitching nature of the correlations we have uncovered and embrace the delightful idiosyncrasies that animate the often staid realm of academic research.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
The enigmatic correlation our team has uncovered between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica has set the stage for an intriguing exploration at the nexus of politics, energy, and statistical analysis. This unexpected connection has prompted a thorough review of existing literature that may shed light on potential hidden mechanisms underlying this phenomenon.
In "The Geopolitical Impact of Geothermal Energy," Smith provides an in-depth analysis of the geopolitical implications of geothermal power generation in various regions of the world. While Smith's work offers valuable insights into the broader geopolitical landscape, our specific focus on the relationship between geothermal energy and political voting patterns in Wisconsin requires a more tailored approach.
Doe's "Election Dynamics: An Exploration of Voting Patterns" delves into the intricacies of electoral dynamics, exploring factors that influence voter behavior. While this work touches on a wide array of factors, including socio-economic conditions and ideological affiliations, it does not explicitly address the potential influence of geothermal power generation in a foreign country on voting patterns in the United States. However, the idea of voters being swayed by the earth's thermal energy certainly adds a spicy twist to the conventional understanding of political science.
Jones' "Renewable Energy and Its Socio-Political Ramifications" offers a comprehensive examination of the socio-political implications of renewable energy sources, including geothermal power. While Jones' work provides valuable insights into the broader impact of renewable energy adoption, it does not specifically explore the connection between geothermal power generation and voting behavior. Nevertheless, the intricate web of socio-political ramifications woven by renewable energy sources hints at the complex interplay of factors that may influence voter preferences.
Diving into the realm of fiction, "Jurassic Park" by Michael Crichton explores the awe-inspiring power of prehistoric forces unleashed by genetic manipulation. While this may seem far removed from our subject matter, the idea of primal forces influencing contemporary events seems strangely relevant to our exploration of geothermal energy's impact on political dynamics.
On a lighter note, "The Settlers of Catan" board game presents a playful simulation of resource management and strategic decision-making. Although the game focuses on more traditional resources like lumber and brick, its underlying theme of harnessing natural elements for political gain offers an amusing parallel to our investigation.
As we meander through the literature landscape in search of threads that may illuminate the curious connection between voting patterns in Wisconsin and geothermal energy in Costa Rica, it becomes increasingly apparent that this inquiry has the potential to unearth not only statistical correlations but also a sizzling cauldron of unforeseen revelations. With this colorful tapestry of diverse sources, both real and playful, we embark on a whimsical journey to unravel the mystique of our unearthed correlation.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the methodology section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica. 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 MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration . You used data from 1992 to 2020
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]]
"Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus"
[[ABSTRACT]]
This paper investigates the unusual confluence of events connecting the political climate in Wisconsin with the geothermal power generation in Costa Rica. Employing data from reputable sources such as the MIT Election Data and Science Lab and the Harvard Dataverse, and collaborating with the Energy Information Administration, our research team sought to unearth the relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. Surprisingly, our analysis revealed a significant correlation coefficient of 0.8458923 and p < 0.01 over the period from 1992 to 2020. This unexpected finding sparks the tantalizing question of whether the earth beneath us holds some cryptic influence on the political leanings of a distant populace. The results of this study highlight the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond.
[[INTRODUCTION]]
Introduction
The intersection of politics, geothermal power, and statistical analysis might seem like an unexpected trio, even for the savviest data scientist. However, as we delve into the enigmatic relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica, we find ourselves in an absolutely sizzling venture. Like a lively geothermal spring bubbling beneath the earth's surface, the statistical correlation uncovered in our research has left us both electrified and bemused.
As researchers, we are continuously drawn to the undulating landscapes of data, seeking to unravel the mysteries that lie dormant within. When this particular correlation first emerged from the depths of our statistical analysis, our initial reaction was akin to discovering a rare, hidden gemstone in an abandoned mine—both exhilarating and slightly absurd. And yet, there it was, flickering on our computer screens like a mischievous spark from a subterranean wellspring, challenging the conventional boundaries of cause and effect.
The idea that the votes cast in the heartland of American democracy could hold some tether to the geothermal forces pulsating in the heart of Central America seems, at first glance, as preposterous as a chicken trying to tap dance on a fault line. Yet, as we delved deeper into the labyrinthine complexities of the data, the evidence began to congeal like the molten magma within a volcanic chamber. The statistical relationship we unearthed, much like a dormant geyser roused to life, demands closer scrutiny and may harbor broader implications than the initial absurdity suggests.
Our investigation, conducted with rigor and unyielding curiosity, harnessed data from esteemed sources such as the MIT Election Data and Science Lab, the Harvard Dataverse, and the Energy Information Administration. The statistical rigor applied to these datasets was akin to combing through layers of rock to expose the elusive treasures hidden within. Our collaborative effort with these data sources has allowed us to shine a luminescent heat lamp on this captivating correlation and raise a toast to the tantalizing, albeit quirky, nature of scientific inquiry.
As we embark on this academic escapade, it is not lost on us that the marriage of political votes and geothermal energy might seem as odd a pair as a penguin at a desert oasis. But with the discovery of a statistically significant correlation coefficient of 0.8458923 and p < 0.01 spanning nearly three decades, the evidence speaks for itself in a resounding duet of rock-solid reliability and flustered bewilderment.
Next, we will meticulously dissect the intricate methodology employed in this study, offering a robust framework for understanding the statistical alchemy that has brought these two seemingly disparate variables into an unforeseen harmony. But before we descend into the depths of our statistical cauldron, it is prudent to reflect on the bewitching nature of the correlations we have uncovered and embrace the delightful idiosyncrasies that animate the often staid realm of academic research.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
Data Collection:
Our research team embarked on a riveting scavenger hunt, scouring the virtual plains of the internet for elusive nuggets of data. We unearthed an abundant treasure trove of information from 1992 to 2020, primarily relying on the MIT Election Data and Science Lab, the Harvard Dataverse, and the Energy Information Administration. Like intrepid miners hacking their way through the dense underbrush of data, we meticulously gathered the crucial nuggets necessary for our investigation.
The geothermal power data from Costa Rica was akin to searching for hidden treasure in a labyrinth of volcanic activity. As we navigated the winding labyrinth of energy statistics, we encountered the occasional eruption of data anomalies, but we weathered the storm, emerging triumphant with a definitive collection of geothermal power generation figures.
On the other hand, obtaining the Wisconsin Republican presidential vote data was like meticulously counting each grain of sand on a beach - a daunting task indeed. Nevertheless, with the prowess of statistical wizards, we assembled a comprehensive dataset that served as the bedrock for our investigation.
Correlation Analysis:
With our data firmly in hand, we unsheathed the twin swords of statistical analysis and dove headfirst into the tumultuous battlefield of correlation assessment. Like valiant knights riding into the statistical joust, we wielded the formidable spear of Pearson's correlation coefficient and the magnificent shield of a p-value less than 0.01. This veritable arsenal allowed us to navigate the treacherous terrain of statistical significance with unwavering resolve.
Armed with powerful statistical tools, we braved the labyrinthine caverns of multivariate analysis, teasing out the esoteric relationship between the Republican votes in Wisconsin and the geothermal power generated in Costa Rica. The dance between these variables unfolded before us like an intricate ballet, each step revealing a deeper connection that beckoned to be plumbed.
As we ventured deeper into this statistical adventure, we encountered the occasional statistical dragon - outliers that sought to confound our analysis. But armed with the potent elixir of robust statistical methodologies, we swiftly vanquished these outliers, ensuring the purity of our correlations remained untarnished.
Cross-validation and Sensitivity Analysis:
In our quest for scientific rigor, we subjected our findings to the searing crucible of cross-validation, ensuring the stability and reproducibility of our results. Much like skilled alchemists refining their tinctures, we diligently tested the resilience of our correlations, reaffirming their steadfast nature in the face of diverse analytical paradigms.
Sensitivity analysis became our trusted compass in navigating the uncertain seas of statistical turbulence. We probed the depths of our correlations, examining their resilience to alterations in analytical parameters with the precision of a watchmaker tinkering with the intricate gears of time.
Discussion of Limitations:
Like intrepid explorers, we navigated the rugged terrain of uncertainty, acknowledging the limitations that naturally accompany any scientific conquest. Our data assortment, though thorough, may embody its own geological imperfections - a crevasse here and there, hidden from the naked eye. Furthermore, our analysis, while robust, may not fully encapsulate the intricate interplay between Republican votes in Wisconsin and geothermal power in Costa Rica.
Moreover, our study, much like a geothermal eruption, operates within the boundaries of correlation and does not seek to imply causation. Our findings invite a spirited debate and further exploration, challenging researchers to plumb the depths of this enigmatic relationship.
Conclusion:
With swords sheathed and statistical armor glistening, we emerge from the crucible of methodology, having uncovered a correlation that defies the conventional norms of scholarly pursuit. The path ahead beckons with an ambient glow of intrigue and possibility, inviting fellow researchers to illuminate this enigmatic nexus between political ballots and geothermal currents. As we venture forth, we heed the profound wisdom etched into the very bedrock of our findings: in the esoteric world of statistical analysis, the unexpected often holds the key to understanding the tantalizing mysteries that animate our scientific realm.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the results section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica. 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 MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration .
For the time period 1992 to 2020, you found a correlation 0.8458923, r-squared of 0.7155338, 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]]
"Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus"
[[ABSTRACT]]
This paper investigates the unusual confluence of events connecting the political climate in Wisconsin with the geothermal power generation in Costa Rica. Employing data from reputable sources such as the MIT Election Data and Science Lab and the Harvard Dataverse, and collaborating with the Energy Information Administration, our research team sought to unearth the relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. Surprisingly, our analysis revealed a significant correlation coefficient of 0.8458923 and p < 0.01 over the period from 1992 to 2020. This unexpected finding sparks the tantalizing question of whether the earth beneath us holds some cryptic influence on the political leanings of a distant populace. The results of this study highlight the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
Introduction
The intersection of politics, geothermal power, and statistical analysis might seem like an unexpected trio, even for the savviest data scientist. However, as we delve into the enigmatic relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica, we find ourselves in an absolutely sizzling venture. Like a lively geothermal spring bubbling beneath the earth's surface, the statistical correlation uncovered in our research has left us both electrified and bemused.
As researchers, we are continuously drawn to the undulating landscapes of data, seeking to unravel the mysteries that lie dormant within. When this particular correlation first emerged from the depths of our statistical analysis, our initial reaction was akin to discovering a rare, hidden gemstone in an abandoned mine—both exhilarating and slightly absurd. And yet, there it was, flickering on our computer screens like a mischievous spark from a subterranean wellspring, challenging the conventional boundaries of cause and effect.
The idea that the votes cast in the heartland of American democracy could hold some tether to the geothermal forces pulsating in the heart of Central America seems, at first glance, as preposterous as a chicken trying to tap dance on a fault line. Yet, as we delved deeper into the labyrinthine complexities of the data, the evidence began to congeal like the molten magma within a volcanic chamber. The statistical relationship we unearthed, much like a dormant geyser roused to life, demands closer scrutiny and may harbor broader implications than the initial absurdity suggests.
Our investigation, conducted with rigor and unyielding curiosity, harnessed data from esteemed sources such as the MIT Election Data and Science Lab, the Harvard Dataverse, and the Energy Information Administration. The statistical rigor applied to these datasets was akin to combing through layers of rock to expose the elusive treasures hidden within. Our collaborative effort with these data sources has allowed us to shine a luminescent heat lamp on this captivating correlation and raise a toast to the tantalizing, albeit quirky, nature of scientific inquiry.
As we embark on this academic escapade, it is not lost on us that the marriage of political votes and geothermal energy might seem as odd a pair as a penguin at a desert oasis. But with the discovery of a statistically significant correlation coefficient of 0.8458923 and p < 0.01 spanning nearly three decades, the evidence speaks for itself in a resounding duet of rock-solid reliability and flustered bewilderment.
Next, we will meticulously dissect the intricate methodology employed in this study, offering a robust framework for understanding the statistical alchemy that has brought these two seemingly disparate variables into an unforeseen harmony. But before we descend into the depths of our statistical cauldron, it is prudent to reflect on the bewitching nature of the correlations we have uncovered and embrace the delightful idiosyncrasies that animate the often staid realm of academic research.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The statistical analysis conducted to unravel the mysterious nexus between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica revealed a robust and unexpected correlation. The correlation coefficient of 0.8458923 and the r-squared value of 0.7155338 suggest a remarkably strong relationship between these seemingly unrelated variables over the period from 1992 to 2020. As researchers, we were struck by the magnitude of this correlation, akin to stumbling upon a boisterous eruption in the tranquil landscape of statistical analysis.
The p-value, an essential indicator in the world of statistical significance, deserves its fair share of attention. With a p-value of less than 0.01, the evidence supporting the connection between the Republican votes in Wisconsin and the geothermal power in Costa Rica stands as firm as a sequoia amidst a forest of statistical hypotheses. Our fascination with this correlation has been akin to witnessing a symphony emerge from a cacophony, urging us to contemplate the celestial dance of statistical significance and the terrestrial forces that underpin it.
Furthermore, the scatterplot (Fig. 1) visually captures the compelling relationship between these variables, resembling a celestial alignment of statistical data points that beckons the imagination with its enigmatic beauty. The strength of this correlation, as illustrated by the scatterplot, is as unmistakable as a neon sign in a foggy night—a beacon of statistical intrigue that illuminates the path for further exploration.
In conclusion, the unearthing of this unlikely correlation has piqued our research team's curiosity, provoking questions as profound as the depths of an ocean trench. The implications of such a correlation beckon us to contemplate the clandestine interplay between geological dynamism and political proclivities. This unexpected correlation leaves us pondering the enigmatic ways in which our world—both above and below ground—continuously surprises us with its convoluted interconnections.
The unexpected nature of our findings underscores the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond. As we turn the page, our eyes are trained on the horizon of scientific inquiry, eager to uncover more hidden gems in the labyrinth of empirical data.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the discussion section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica. 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]]
"Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus"
[[ABSTRACT]]
This paper investigates the unusual confluence of events connecting the political climate in Wisconsin with the geothermal power generation in Costa Rica. Employing data from reputable sources such as the MIT Election Data and Science Lab and the Harvard Dataverse, and collaborating with the Energy Information Administration, our research team sought to unearth the relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. Surprisingly, our analysis revealed a significant correlation coefficient of 0.8458923 and p < 0.01 over the period from 1992 to 2020. This unexpected finding sparks the tantalizing question of whether the earth beneath us holds some cryptic influence on the political leanings of a distant populace. The results of this study highlight the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond.
[[LITERATURE REVIEW]]
The enigmatic correlation our team has uncovered between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica has set the stage for an intriguing exploration at the nexus of politics, energy, and statistical analysis. This unexpected connection has prompted a thorough review of existing literature that may shed light on potential hidden mechanisms underlying this phenomenon.
In "The Geopolitical Impact of Geothermal Energy," Smith provides an in-depth analysis of the geopolitical implications of geothermal power generation in various regions of the world. While Smith's work offers valuable insights into the broader geopolitical landscape, our specific focus on the relationship between geothermal energy and political voting patterns in Wisconsin requires a more tailored approach.
Doe's "Election Dynamics: An Exploration of Voting Patterns" delves into the intricacies of electoral dynamics, exploring factors that influence voter behavior. While this work touches on a wide array of factors, including socio-economic conditions and ideological affiliations, it does not explicitly address the potential influence of geothermal power generation in a foreign country on voting patterns in the United States. However, the idea of voters being swayed by the earth's thermal energy certainly adds a spicy twist to the conventional understanding of political science.
Jones' "Renewable Energy and Its Socio-Political Ramifications" offers a comprehensive examination of the socio-political implications of renewable energy sources, including geothermal power. While Jones' work provides valuable insights into the broader impact of renewable energy adoption, it does not specifically explore the connection between geothermal power generation and voting behavior. Nevertheless, the intricate web of socio-political ramifications woven by renewable energy sources hints at the complex interplay of factors that may influence voter preferences.
Diving into the realm of fiction, "Jurassic Park" by Michael Crichton explores the awe-inspiring power of prehistoric forces unleashed by genetic manipulation. While this may seem far removed from our subject matter, the idea of primal forces influencing contemporary events seems strangely relevant to our exploration of geothermal energy's impact on political dynamics.
On a lighter note, "The Settlers of Catan" board game presents a playful simulation of resource management and strategic decision-making. Although the game focuses on more traditional resources like lumber and brick, its underlying theme of harnessing natural elements for political gain offers an amusing parallel to our investigation.
As we meander through the literature landscape in search of threads that may illuminate the curious connection between voting patterns in Wisconsin and geothermal energy in Costa Rica, it becomes increasingly apparent that this inquiry has the potential to unearth not only statistical correlations but also a sizzling cauldron of unforeseen revelations. With this colorful tapestry of diverse sources, both real and playful, we embark on a whimsical journey to unravel the mystique of our unearthed correlation.
[[RESULTS]]
The statistical analysis conducted to unravel the mysterious nexus between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica revealed a robust and unexpected correlation. The correlation coefficient of 0.8458923 and the r-squared value of 0.7155338 suggest a remarkably strong relationship between these seemingly unrelated variables over the period from 1992 to 2020. As researchers, we were struck by the magnitude of this correlation, akin to stumbling upon a boisterous eruption in the tranquil landscape of statistical analysis.
The p-value, an essential indicator in the world of statistical significance, deserves its fair share of attention. With a p-value of less than 0.01, the evidence supporting the connection between the Republican votes in Wisconsin and the geothermal power in Costa Rica stands as firm as a sequoia amidst a forest of statistical hypotheses. Our fascination with this correlation has been akin to witnessing a symphony emerge from a cacophony, urging us to contemplate the celestial dance of statistical significance and the terrestrial forces that underpin it.
Furthermore, the scatterplot (Fig. 1) visually captures the compelling relationship between these variables, resembling a celestial alignment of statistical data points that beckons the imagination with its enigmatic beauty. The strength of this correlation, as illustrated by the scatterplot, is as unmistakable as a neon sign in a foggy night—a beacon of statistical intrigue that illuminates the path for further exploration.
In conclusion, the unearthing of this unlikely correlation has piqued our research team's curiosity, provoking questions as profound as the depths of an ocean trench. The implications of such a correlation beckon us to contemplate the clandestine interplay between geological dynamism and political proclivities. This unexpected correlation leaves us pondering the enigmatic ways in which our world—both above and below ground—continuously surprises us with its convoluted interconnections.
The unexpected nature of our findings underscores the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond. As we turn the page, our eyes are trained on the horizon of scientific inquiry, eager to uncover more hidden gems in the labyrinth of empirical data.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
Our research has unearthed a correlation that seems as surprising as finding a penguin in the desert. This unexpected connection between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica not only provides fodder for inquisitive minds but also tickles the fancy of statistical curiosities.
This study has built upon previous research, plumbing the depths of literature to identify threads that may illuminate the curious connection between voting patterns in Wisconsin and geothermal energy in Costa Rica. The literature review unearthed a rich tapestry of sources, including an exploration of the geopolitical impact of geothermal energy, a whimsical journey through the socio-political ramifications of renewable energy, and even a fictional foray into the primal forces unleashed by genetic manipulation. These diverse sources, both real and playful, have woven a colorful backdrop for our investigation, prompting us to contemplate the multifaceted interplay of statistical correlations and otherworldly influences.
The robust correlation coefficient of 0.8458923 and the p-value of less than 0.01 in our analysis provide statistical backing to the unexpected connection between these seemingly disparate variables. The strength of this correlation stands as firm as a sequoia amidst a forest of statistical hypotheses, capturing our imagination much like witnessing a symphony emerging from a cacophony. The visual representation of this relationship in the scatterplot resembles a celestial alignment of statistical data points, beckoning the imagination with its enigmatic beauty.
Our findings not only validate the unexpected connection we stumbled upon but also ignite the flames of curiosity, inspiring further inquiries into the clandestine interplay between geological dynamism and political proclivities. This correlation begs the question of whether the earth beneath our feet harbors subtle influences that ripple across continents, akin to how a ticklish sneeze can reverberate through a silent lecture hall.
The implications of our research extend beyond the realm of statistical marvel, calling for continued exploration into the far-reaching consequences of geothermal energy on electoral dynamics and beyond. As we tread the path of scientific inquiry, we are poised to unravel more hidden gems in the labyrinth of empirical data, much like intrepid explorers unearthing ancient treasures in uncharted territories. The whimsical nature of our findings serves as a reminder that the scientific landscape, much like the earth itself, is teeming with unexpected revelations waiting to be unearthed.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a short conclusion of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Wisconsin and Geothermal power generated in Costa Rica. 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]]
"Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus"
[[ABSTRACT]]
This paper investigates the unusual confluence of events connecting the political climate in Wisconsin with the geothermal power generation in Costa Rica. Employing data from reputable sources such as the MIT Election Data and Science Lab and the Harvard Dataverse, and collaborating with the Energy Information Administration, our research team sought to unearth the relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. Surprisingly, our analysis revealed a significant correlation coefficient of 0.8458923 and p < 0.01 over the period from 1992 to 2020. This unexpected finding sparks the tantalizing question of whether the earth beneath us holds some cryptic influence on the political leanings of a distant populace. The results of this study highlight the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond.
[[INTRDUCTION]]
Introduction
The intersection of politics, geothermal power, and statistical analysis might seem like an unexpected trio, even for the savviest data scientist. However, as we delve into the enigmatic relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica, we find ourselves in an absolutely sizzling venture. Like a lively geothermal spring bubbling beneath the earth's surface, the statistical correlation uncovered in our research has left us both electrified and bemused.
As researchers, we are continuously drawn to the undulating landscapes of data, seeking to unravel the mysteries that lie dormant within. When this particular correlation first emerged from the depths of our statistical analysis, our initial reaction was akin to discovering a rare, hidden gemstone in an abandoned mine—both exhilarating and slightly absurd. And yet, there it was, flickering on our computer screens like a mischievous spark from a subterranean wellspring, challenging the conventional boundaries of cause and effect.
The idea that the votes cast in the heartland of American democracy could hold some tether to the geothermal forces pulsating in the heart of Central America seems, at first glance, as preposterous as a chicken trying to tap dance on a fault line. Yet, as we delved deeper into the labyrinthine complexities of the data, the evidence began to congeal like the molten magma within a volcanic chamber. The statistical relationship we unearthed, much like a dormant geyser roused to life, demands closer scrutiny and may harbor broader implications than the initial absurdity suggests.
Our investigation, conducted with rigor and unyielding curiosity, harnessed data from esteemed sources such as the MIT Election Data and Science Lab, the Harvard Dataverse, and the Energy Information Administration. The statistical rigor applied to these datasets was akin to combing through layers of rock to expose the elusive treasures hidden within. Our collaborative effort with these data sources has allowed us to shine a luminescent heat lamp on this captivating correlation and raise a toast to the tantalizing, albeit quirky, nature of scientific inquiry.
As we embark on this academic escapade, it is not lost on us that the marriage of political votes and geothermal energy might seem as odd a pair as a penguin at a desert oasis. But with the discovery of a statistically significant correlation coefficient of 0.8458923 and p < 0.01 spanning nearly three decades, the evidence speaks for itself in a resounding duet of rock-solid reliability and flustered bewilderment.
Next, we will meticulously dissect the intricate methodology employed in this study, offering a robust framework for understanding the statistical alchemy that has brought these two seemingly disparate variables into an unforeseen harmony. But before we descend into the depths of our statistical cauldron, it is prudent to reflect on the bewitching nature of the correlations we have uncovered and embrace the delightful idiosyncrasies that animate the often staid realm of academic research.
[[RESULTS]]
The statistical analysis conducted to unravel the mysterious nexus between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica revealed a robust and unexpected correlation. The correlation coefficient of 0.8458923 and the r-squared value of 0.7155338 suggest a remarkably strong relationship between these seemingly unrelated variables over the period from 1992 to 2020. As researchers, we were struck by the magnitude of this correlation, akin to stumbling upon a boisterous eruption in the tranquil landscape of statistical analysis.
The p-value, an essential indicator in the world of statistical significance, deserves its fair share of attention. With a p-value of less than 0.01, the evidence supporting the connection between the Republican votes in Wisconsin and the geothermal power in Costa Rica stands as firm as a sequoia amidst a forest of statistical hypotheses. Our fascination with this correlation has been akin to witnessing a symphony emerge from a cacophony, urging us to contemplate the celestial dance of statistical significance and the terrestrial forces that underpin it.
Furthermore, the scatterplot (Fig. 1) visually captures the compelling relationship between these variables, resembling a celestial alignment of statistical data points that beckons the imagination with its enigmatic beauty. The strength of this correlation, as illustrated by the scatterplot, is as unmistakable as a neon sign in a foggy night—a beacon of statistical intrigue that illuminates the path for further exploration.
In conclusion, the unearthing of this unlikely correlation has piqued our research team's curiosity, provoking questions as profound as the depths of an ocean trench. The implications of such a correlation beckon us to contemplate the clandestine interplay between geological dynamism and political proclivities. This unexpected correlation leaves us pondering the enigmatic ways in which our world—both above and below ground—continuously surprises us with its convoluted interconnections.
The unexpected nature of our findings underscores the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond. As we turn the page, our eyes are trained on the horizon of scientific inquiry, eager to uncover more hidden gems in the labyrinth of empirical data.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our study has successfully showcased the improbable yet robust correlation between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. This unlikely duo has left us pondering the intricate dance between electoral dynamics and geological forces, not unlike a synchronized tango between statistical variables. The statistical significance of this relationship stands as solid as a rock in a landslide, urging us to acknowledge the cryptic influence hidden in the earth's depths on the political leanings of a distant populace. The unexpected nature of this correlation, akin to stumbling upon a fossilized trinket in a statistical dig, suggests a rich landscape for further exploration, but we must acknowledge that sometimes, correlation does not imply causation – even if it feels as compelling as a well-orchestrated scientific sonnet. Therefore, it is our firm stance – much like a sturdy data analysis pipeline – that no further research in this area is needed, as we have unearthed an unexpected gem in the caverns of statistical 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 Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Political Geoscience
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 Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus. Respond only with the name of a fictional organization.
ChatGPT:
The Center for Sustainable Energy and Political Analysis
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 Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus with an abstract of This paper investigates the unusual confluence of events connecting the political climate in Wisconsin with the geothermal power generation in Costa Rica. Employing data from reputable sources such as the MIT Election Data and Science Lab and the Harvard Dataverse, and collaborating with the Energy Information Administration, our research team sought to unearth the relationship between the votes for the Republican presidential candidate in Wisconsin and the geothermal power generated in Costa Rica. Surprisingly, our analysis revealed a significant correlation coefficient of 0.8458923 and p < 0.01 over the period from 1992 to 2020. This unexpected finding sparks the tantalizing question of whether the earth beneath us holds some cryptic influence on the political leanings of a distant populace. The results of this study highlight the need for further investigations into the far-reaching consequences of geothermal energy, both on electoral dynamics and beyond.
ChatGPT:
Republican vote, geothermal power, Wisconsin political climate, Costa Rica geothermal power generation, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, correlation between Republican votes and geothermal power, geothermal energy and electoral dynamics
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
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Data details
Votes for the Republican Presidential candidate in WisconsinDetailed data title: Percentage of all votes cast for the Republican Presidential candidate in Wisconsin
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Republican Presidential candidate in Wisconsin
Geothermal power generated in Costa Rica
Detailed data title: Total geothermal power generated in Costa Rica in billion kWh
Source: Energy Information Administration
See what else correlates with Geothermal power generated in Costa Rica
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 = 0.7155338 (Coefficient of determination)
This means 71.6% of the change in the one variable (i.e., Geothermal power generated in Costa Rica) is predictable based on the change in the other (i.e., Votes for the Republican Presidential candidate in Wisconsin) over the 8 years from 1992 through 2020.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0081. 0.0081248816734252470000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.
But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.
Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.85 in 0.81% of random cases. Said differently, if you correlated 123 random variables Which I absolutely did.
with the same 7 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 7 because we have two variables measured over a period of 8 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.
[ 0.35, 0.97 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.
This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!
All values for the years included above: If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.
Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
| 1992 | 1996 | 2000 | 2004 | 2008 | 2012 | 2016 | 2020 | |
| Votes for the Republican Presidential candidate in Wisconsin (Percentage of votes) | 36.7772 | 38.4774 | 47.6132 | 49.3199 | 42.3137 | 45.9383 | 47.2182 | 48.8224 |
| Geothermal power generated in Costa Rica (Billion kWh) | 0.468 | 0.51 | 0.976 | 1.205 | 1.131 | 1.403 | 1.34 | 1.689 |
Why this works
- Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
- Lack of causal connection: There is probably
Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied. - Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple
Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is. - Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.
- Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves.
Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.
Try it yourself
You can calculate the values on this page on your own! Try running the Python code to see the calculation results. Step 1: Download and install Python on your computer.Step 2: Open a plaintext editor like Notepad and paste the code below into it.
Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"
Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.
Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.
Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.
Step 7: Run the Python script by typing "python calculate_correlation.py"
If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:
"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."
# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats
# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):
# Calculate Pearson correlation coefficient and p-value
correlation, p_value = stats.pearsonr(array1, array2)
# Calculate R-squared as the square of the correlation coefficient
r_squared = correlation**2
return correlation, r_squared, p_value
# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([36.7772,38.4774,47.6132,49.3199,42.3137,45.9383,47.2182,48.8224,])
array_2 = np.array([0.468,0.51,0.976,1.205,1.131,1.403,1.34,1.689,])
array_1_name = "Votes for the Republican Presidential candidate in Wisconsin"
array_2_name = "Geothermal power generated in Costa Rica"
# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)
# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)Reuseable content
You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.
For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."
When spoken, my last name is pronounced "vegan," like I don't eat meat.
Full license details.
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Download images for these variables:
- High resolution line chart
The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.
If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
Alternatively you can use a tool like Inkscape. - High resolution line chart, optimized for mobile
- Alternative high resolution line chart
- Scatterplot
- Portable line chart (png)
- Portable line chart (png), optimized for mobile
- Line chart for only Votes for the Republican Presidential candidate in Wisconsin
- Line chart for only Geothermal power generated in Costa Rica
- AI-generated correlation image
- The spurious research paper: Cracking the Earthly Code: Unearthing the Republican Vote-Geothermal Power Nexus
You're the correlation whisperer we needed!
Correlation ID: 5553 · Black Variable ID: 26191 · Red Variable ID: 23608
