Spurious correlation #4,965 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for the Democrat Presidential candidate in South Dakota and the second variable is Hydopower energy generated in El Salvador. The chart goes from 1980 to 2020, 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 Votes for the Democrat Presidential candidate in South Dakota and the second variable is Hydopower energy generated in El Salvador. The chart goes from 1980 to 2020, and the two variables track closely in value over that time.

AI explanation

As more Democratic votes flowed in South Dakota, it created a positive current that was harnessed by electoral engineers, leading to a surge in hydro-power energy production in El Salvador. It seems like the political race sparked a shocking amount of energy, and the candidates were definitely making waves, creating a real power struggle!

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Generate an image of a futuristic, cybernetic landscape blending the natural beauty of South Dakota with the modern infrastructure of El Salvador. Show a stream of glowing blue particles flowing into ballot boxes, symbolizing votes, while powerful turbines with glowing energy harness the rushing water to generate hydroelectric power. The scene should convey a sense of political energy and power struggle, with a blend of natural and technological elements. 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 Democrat Presidential candidate in South Dakota and Hydopower energy generated in El Salvador. 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: Vaporwave Surrealism A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Data cyber art Concept art
ChatGPT: Data cyber art

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Votes for the Democrat Presidential candidate in South Dakota and Hydopower energy generated in El Salvador, in the following situation: As more Democratic votes flowed in South Dakota, it created a positive current that was harnessed by electoral engineers, leading to a surge in hydro-power energy production in El Salvador. It seems like the political race sparked a shocking amount of energy, and the candidates were definitely making waves, creating a real power struggle!. Keep the description simple.
ChatGPT: In South Dakota, a stream of blue votes flows steadily into the ballot boxes, creating a current of positivity. Meanwhile, in El Salvador, powerful turbines harness the energy from rushing water, generating a surge in hydroelectric power. The political race seems to have sparked a shocking amount of energy. The candidates are making waves and creating a real power struggle!

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: [ Data cyber art ] and depicting this scene: In South Dakota, a stream of blue votes flows steadily into the ballot boxes, creating a current of positivity. Meanwhile, in El Salvador, powerful turbines harness the energy from rushing water, generating a surge in hydroelectric power. The political race seems to have sparked a shocking amount of energy. The candidates are making waves and creating a real power struggle! Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image of a futuristic, cybernetic landscape blending the natural beauty of South Dakota with the modern infrastructure of El Salvador. Show a stream of glowing blue particles flowing into ballot boxes, symbolizing votes, while powerful turbines with glowing energy harness the rushing water to generate hydroelectric power. The scene should convey a sense of political energy and power struggle, with a blend of natural and technological elements.

*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 Votes for the Democrat Presidential candidate in South Dakota caused Hydopower energy generated in El Salvador to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy

The Journal of Transnational Water Governance

Jan 2024

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in South Dakota and Hydopower energy generated in El Salvador. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy"

[[ABSTRACT]]
In this hydro-politically charged study, we delve into the surprising correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embark on a wild journey to uncover if there's more than meets the eye in this unlikely political and energy dance.

With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. We navigate the political landscape and energy currents to reveal the intricate web of connections between seemingly unrelated phenomena. While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a flow of influence between electoral choices in South Dakota and the hydropower energy landscape in El Salvador.

In this electrifying study, we not only shed light on this unexpected connection but also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics and energize your understanding of interconnected global phenomena.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The world of political science and energy economics is often a whirlpool of data, theories, and unexpected relationships. In this hydro-politically charged study, we dive headfirst into the curious case of the connection between votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. As researchers, we are often encouraged to "think outside the box," and in this case, we took it quite literally, venturing beyond national borders and across disciplines to uncover this surprising correlation.
The journey to unravel this hydro-political mystery began with the realization that while South Dakota may be famously known for Mount Rushmore, pheasant hunting, and, of course, its strong support for the agricultural sector, its influence may extend beyond state lines and into the hydropower energy landscape of El Salvador. The intricate web of connections between seemingly unrelated phenomena often leaves researchers in a state of shock, akin to electrical overload.
Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embarked on a wild and watery adventure to determine if there's more than meets the eye in this unexpected political and energy dance. The data flowed like a mighty river, guiding us through statistical eddies and political rip currents, ultimately leading to the beacon of correlation.
With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. The statistical significance of this relationship practically shouted at us like a waterfall in a serene forest, impossible to ignore. It's as if the data itself whispered, "Let me be your current guide to unveil the hydro-political connection."
While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a certain synergy, a certain ebb and flow, between electoral choices in South Dakota and the hydropower energy landscape in El Salvador. This unexpected relationship between political preference and energy generation sent ripples through our understanding of global interconnectedness, leaving us in awe of the unseen influences shaping our world.
In this electrifying study, we not only shed light on this unexpected connection – we also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics, and together, let's energize our understanding of the interconnected global phenomena that power the world – both literally and metaphorically.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The surprising correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador has sparked debates and raised eyebrows in academic and non-academic circles alike. While this peculiar relationship may seem like a chance alignment of stars, upon close inspection, it reveals an intricate dance of political preferences and energy dynamics. In "Smith et al.'s Study of Unexpected Political and Energy Correlations," the authors find lorem and ipsum. Similarly, Doe and Jones, in their respective works, "The Political Hydro-Quandary" and "Energetic Electoral Intricacies," delve into the depths of this unexpected connection, further adding to the growing body of research on this enigmatic relationship.

In the realm of energy economics, the literature offers insight into the complex interplay between hydroelectric power generation and political landscapes. Works such as "Hydropower and Global Politics" by Waters et al. and "The Currents of Energy: A Political Economy Perspective" by Watts provide a comprehensive understanding of the geopolitical significance of hydropower energy, though they may have missed the wave of electoral influence in their analyses.

On the political front, books such as "The Art of Winning Votes" by Election and "Democracy in Action: A Comprehensive Analysis" by Politics & Power shed light on the intricacies of electoral preferences and decision-making processes. However, none of these sources anticipated the electrifying link between the South Dakotan vote and Salvadoran hydropower energy. It's as if they overlooked the shocking synergy, the charged connection that animates our research!

Turning to slightly more unconventional sources, one could argue that the fictional works of "The Power Games" by Suzanne Collaboration and "Currents of Change" by J.K. Ruling are, albeit unintentionally, prescient in capturing the underlying essence of our unexpected findings. These novels, in their own fantastical ways, hint at the hidden currents and power struggles that underpin our real-world hydro-political discovery.

But let's not forget the world of board games! Titles like "Power Struggle: The Political Game" and "Hydro Heroes: Quest for Energy" could provide us with a fresh perspective on the playful parallels to our research. Perhaps in these games lie the clues – the elusive keys – to unraveling the mysteries of hydro-political connections with a twist of fun!

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in South Dakota and Hydopower energy generated in El Salvador. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration . You used data from 1980 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]]
"Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy"

[[ABSTRACT]]
In this hydro-politically charged study, we delve into the surprising correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embark on a wild journey to uncover if there's more than meets the eye in this unlikely political and energy dance.

With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. We navigate the political landscape and energy currents to reveal the intricate web of connections between seemingly unrelated phenomena. While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a flow of influence between electoral choices in South Dakota and the hydropower energy landscape in El Salvador.

In this electrifying study, we not only shed light on this unexpected connection but also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics and energize your understanding of interconnected global phenomena.

[[INTRODUCTION]]
The world of political science and energy economics is often a whirlpool of data, theories, and unexpected relationships. In this hydro-politically charged study, we dive headfirst into the curious case of the connection between votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. As researchers, we are often encouraged to "think outside the box," and in this case, we took it quite literally, venturing beyond national borders and across disciplines to uncover this surprising correlation.
The journey to unravel this hydro-political mystery began with the realization that while South Dakota may be famously known for Mount Rushmore, pheasant hunting, and, of course, its strong support for the agricultural sector, its influence may extend beyond state lines and into the hydropower energy landscape of El Salvador. The intricate web of connections between seemingly unrelated phenomena often leaves researchers in a state of shock, akin to electrical overload.
Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embarked on a wild and watery adventure to determine if there's more than meets the eye in this unexpected political and energy dance. The data flowed like a mighty river, guiding us through statistical eddies and political rip currents, ultimately leading to the beacon of correlation.
With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. The statistical significance of this relationship practically shouted at us like a waterfall in a serene forest, impossible to ignore. It's as if the data itself whispered, "Let me be your current guide to unveil the hydro-political connection."
While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a certain synergy, a certain ebb and flow, between electoral choices in South Dakota and the hydropower energy landscape in El Salvador. This unexpected relationship between political preference and energy generation sent ripples through our understanding of global interconnectedness, leaving us in awe of the unseen influences shaping our world.
In this electrifying study, we not only shed light on this unexpected connection – we also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics, and together, let's energize our understanding of the interconnected global phenomena that power the world – both literally and metaphorically.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To uncover the dazzling connection between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador, we embarked on an exhilarating methodological journey that would make even the most stodgy researcher consider donning a pair of water wings. Our data collection process resembled a quest for hidden treasure, scouring the digital seas of the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration to ensure that our findings weren't just a mirage in the statistical desert.

First, to measure the votes for the Democratic presidential candidate in South Dakota, we dived deep into the historical electoral data from 1980 to 2020, akin to searching for a lost shipwreck in uncharted statistical waters. We employed rigorous statistical analysis and charted a course through the waves of demographic trends, political inclinations, and even the occasional political maelstrom to ensure that our data was as robust as an ocean liner.

Navigating the complex currents of hydropower energy generation in El Salvador, we harnessed the energy of the vast internet sea, seeking out databases and reports like intrepid sailors searching for clues on a treasure map. We meticulously charted the annual hydropower energy generation, meticulously examining the ebbs and flows of energy production to ensure that our data wasn't a mere puddle of misinformation.

The analysis itself was a whirlpool of statistical tests, resembling a high-stakes poker game where the river card could make or break our theoretical boat. Employing sophisticated software and procedures, we calculated correlation coefficients and performed regression analyses, ensuring that our findings weren't just a statistical flotsam adrift in the sea of significance.

Our methodological approach mirrored a scientific expedition, one where the path was as unpredictable as an ocean storm, and the destination was as mythical as the fabled city of Atlantis. We navigated each research method with a sense of adventure, constantly adjusting our sails to avoid statistical shipwrecks and methodological krakens that could have sunk our research ship faster than you can say "statistical outlier."

In the end, our approach was as unconventional as a seafaring scientist with a penchant for puns, but we're confident that our findings will make a splash in the academic community and leave our readers buoyed by the unexpected connections we uncovered. So, batten down the hatches and prepare to embark on this methodological odyssey, where statistical truth and hydro-political intrigue collide in a tempest of intellectual inquiry.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 Democrat Presidential candidate in South Dakota and Hydopower energy generated in El Salvador. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration .

For the time period 1980 to 2020, you found a correlation 0.8053368, r-squared of 0.6485673, 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]]
"Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy"

[[ABSTRACT]]
In this hydro-politically charged study, we delve into the surprising correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embark on a wild journey to uncover if there's more than meets the eye in this unlikely political and energy dance.

With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. We navigate the political landscape and energy currents to reveal the intricate web of connections between seemingly unrelated phenomena. While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a flow of influence between electoral choices in South Dakota and the hydropower energy landscape in El Salvador.

In this electrifying study, we not only shed light on this unexpected connection but also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics and energize your understanding of interconnected global phenomena.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The world of political science and energy economics is often a whirlpool of data, theories, and unexpected relationships. In this hydro-politically charged study, we dive headfirst into the curious case of the connection between votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. As researchers, we are often encouraged to "think outside the box," and in this case, we took it quite literally, venturing beyond national borders and across disciplines to uncover this surprising correlation.
The journey to unravel this hydro-political mystery began with the realization that while South Dakota may be famously known for Mount Rushmore, pheasant hunting, and, of course, its strong support for the agricultural sector, its influence may extend beyond state lines and into the hydropower energy landscape of El Salvador. The intricate web of connections between seemingly unrelated phenomena often leaves researchers in a state of shock, akin to electrical overload.
Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embarked on a wild and watery adventure to determine if there's more than meets the eye in this unexpected political and energy dance. The data flowed like a mighty river, guiding us through statistical eddies and political rip currents, ultimately leading to the beacon of correlation.
With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. The statistical significance of this relationship practically shouted at us like a waterfall in a serene forest, impossible to ignore. It's as if the data itself whispered, "Let me be your current guide to unveil the hydro-political connection."
While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a certain synergy, a certain ebb and flow, between electoral choices in South Dakota and the hydropower energy landscape in El Salvador. This unexpected relationship between political preference and energy generation sent ripples through our understanding of global interconnectedness, leaving us in awe of the unseen influences shaping our world.
In this electrifying study, we not only shed light on this unexpected connection – we also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics, and together, let's energize our understanding of the interconnected global phenomena that power the world – both literally and metaphorically.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our hydro-politically charged study revealed a surprisingly robust correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. With a correlation coefficient of 0.8053368 and an r-squared of 0.6485673, it's safe to say that this connection is no mere drop in the bucket. The p-value of less than 0.01 further solidifies the statistical significance of this relationship, much like how a dam fortifies a river.

To visually capture this striking relationship, we present the scatterplot in Figure 1. This plot elegantly showcases the strong positive correlation between the two variables, almost like a synchronized dance between South Dakota's voting preferences and El Salvador's hydropower energy generation. It's as if these variables are performing a watery waltz across national boundaries, defying conventional expectations with their harmonious rhythm.

The strength of this correlation has left us feeling like we've stumbled upon an unexpected treasure, reminiscent of stumbling upon a hidden waterfall in the midst of a dense forest. While some may be tempted to dismiss this finding as mere coincidence, our rigorous analysis suggests that there's more to this hydro-political tango than meets the eye. This discovery has certainly made a splash in the field of political science and energy economics, leaving us all wet with excitement at the prospects of uncovering such unexpected connections in the future.

In conclusion, our research not only highlights the surprising correlation between electoral preferences in South Dakota and hydropower energy generation in El Salvador, but also serves as a reminder of the captivating complexity of interconnected global phenomena. This study urges researchers to dive deep into the multidimensional currents of socio-political and energy systems, embracing the unexpected waves of insight that may wash ashore. So, let's continue to navigate the hydro-political landscape with open minds and a willingness to ride the waves of discovery, for there may be more hydro-political mysteries waiting to surface.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in South Dakota and Hydopower energy generated in El Salvador. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy"

[[ABSTRACT]]
In this hydro-politically charged study, we delve into the surprising correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embark on a wild journey to uncover if there's more than meets the eye in this unlikely political and energy dance.

With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. We navigate the political landscape and energy currents to reveal the intricate web of connections between seemingly unrelated phenomena. While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a flow of influence between electoral choices in South Dakota and the hydropower energy landscape in El Salvador.

In this electrifying study, we not only shed light on this unexpected connection but also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics and energize your understanding of interconnected global phenomena.

[[LITERATURE REVIEW]]
The surprising correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador has sparked debates and raised eyebrows in academic and non-academic circles alike. While this peculiar relationship may seem like a chance alignment of stars, upon close inspection, it reveals an intricate dance of political preferences and energy dynamics. In "Smith et al.'s Study of Unexpected Political and Energy Correlations," the authors find lorem and ipsum. Similarly, Doe and Jones, in their respective works, "The Political Hydro-Quandary" and "Energetic Electoral Intricacies," delve into the depths of this unexpected connection, further adding to the growing body of research on this enigmatic relationship.
In the realm of energy economics, the literature offers insight into the complex interplay between hydroelectric power generation and political landscapes. Works such as "Hydropower and Global Politics" by Waters et al. and "The Currents of Energy: A Political Economy Perspective" by Watts provide a comprehensive understanding of the geopolitical significance of hydropower energy, though they may have missed the wave of electoral influence in their analyses.
On the political front, books such as "The Art of Winning Votes" by Election and "Democracy in Action: A Comprehensive Analysis" by Politics & Power shed light on the intricacies of electoral preferences and decision-making processes. However, none of these sources anticipated the electrifying link between the South Dakotan vote and Salvadoran hydropower energy. It's as if they overlooked the shocking synergy, the charged connection that animates our research!
Turning to slightly more unconventional sources, one could argue that the fictional works of "The Power Games" by Suzanne Collaboration and "Currents of Change" by J.K. Ruling are, albeit unintentionally, prescient in capturing the underlying essence of our unexpected findings. These novels, in their own fantastical ways, hint at the hidden currents and power struggles that underpin our real-world hydro-political discovery.
But let's not forget the world of board games! Titles like "Power Struggle: The Political Game" and "Hydro Heroes: Quest for Energy" could provide us with a fresh perspective on the playful parallels to our research. Perhaps in these games lie the clues – the elusive keys – to unraveling the mysteries of hydro-political connections with a twist of fun!

[[RESULTS]]
The results of our hydro-politically charged study revealed a surprisingly robust correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. With a correlation coefficient of 0.8053368 and an r-squared of 0.6485673, it's safe to say that this connection is no mere drop in the bucket. The p-value of less than 0.01 further solidifies the statistical significance of this relationship, much like how a dam fortifies a river.
To visually capture this striking relationship, we present the scatterplot in Figure 1. This plot elegantly showcases the strong positive correlation between the two variables, almost like a synchronized dance between South Dakota's voting preferences and El Salvador's hydropower energy generation. It's as if these variables are performing a watery waltz across national boundaries, defying conventional expectations with their harmonious rhythm.
The strength of this correlation has left us feeling like we've stumbled upon an unexpected treasure, reminiscent of stumbling upon a hidden waterfall in the midst of a dense forest. While some may be tempted to dismiss this finding as mere coincidence, our rigorous analysis suggests that there's more to this hydro-political tango than meets the eye. This discovery has certainly made a splash in the field of political science and energy economics, leaving us all wet with excitement at the prospects of uncovering such unexpected connections in the future.
In conclusion, our research not only highlights the surprising correlation between electoral preferences in South Dakota and hydropower energy generation in El Salvador, but also serves as a reminder of the captivating complexity of interconnected global phenomena. This study urges researchers to dive deep into the multidimensional currents of socio-political and energy systems, embracing the unexpected waves of insight that may wash ashore. So, let's continue to navigate the hydro-political landscape with open minds and a willingness to ride the waves of discovery, for there may be more hydro-political mysteries waiting to surface.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

In the ripples of our hydro-politically charged findings, we are compelled to dive into the depths of this surprising correlation between votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. Our results not only buoy the existing research but also add a refreshing wave of insight into the interplay of political preferences and energy dynamics on a global scale.

Recalling our literature review, let's address the elephant in the room – or should we say, the salmon in the stream? The unconventional sources we referenced – the board games and even the fictional works – might have seemed like a playful detour. Still, they do hold a droplet of truth. Just as a board game strategist navigates the twists and turns of political power, our research reveals the intricate maneuvers and strategic alliances that underpin the hydro-political landscape. The seemingly prescient hints from fictional narratives now appear as subtle whispers of an underlying reality, akin to a scientific Easter egg hidden in plain sight.

Our findings align with Smith et al.'s and the eloquently named Doe and Jones' studies, illuminating the deeper currents of electoral influence on the hydroenergy tango. It's as if each study adds another layer to this multifaceted tapestry, like the buildup of sedimentary layers in a riverbed, revealing the intricate history and interconnectedness of our hydro-political discovery. Much like connecting the positive and negative terminals of a battery, our results have simply electrified the existing knowledge, shedding new light on the charged relationship between seemingly disparate variables.

Turning to the nuts and bolts of our statistical analysis, the robust correlation coefficient and the near-magical p-value cement the validity of our findings, much like a sturdy dam withstands the relentless flow of a river. It's as if statistics, and perhaps a touch of hydro-magic, have conspired to unveil this unexpected yet undeniable relationship, leaving us awash with a sense of scientific wonder.

The dance captured in our scatterplot, with its synchronized waltz between voting preferences and energy generation, feels like a playful nod from Mother Nature herself, inviting us to partake in the harmonious rhythm of interconnected phenomena. It's a vivid reminder that in the world of research, even the seemingly unassuming variables can sway and sway with the grace of a leaf in the wind, revealing unexpected patterns and connections in the process.

As we navigate the complex, interconnected currents of the socio-political and energy landscape, our study serves as a buoy in the sea of academic inquiry, signaling the presence of uncharted hydro-political territories waiting to be explored. With the current of knowledge flowing ever forward, our research encourages scholars to embrace the unexpected whirlpools of insight, for who knows what hydro-political secrets and discoveries may lie just beneath the surface?

So, let's continue to ride the waves of wonder and inquiry, for in the deep waters of research, there's always a chance of stumbling upon a mesmerizing hydro-political revelation – and isn't that what keeps us all afloat in the sea of academia?

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in South Dakota and Hydopower energy generated in El Salvador. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy"

[[ABSTRACT]]
In this hydro-politically charged study, we delve into the surprising correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embark on a wild journey to uncover if there's more than meets the eye in this unlikely political and energy dance.

With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. We navigate the political landscape and energy currents to reveal the intricate web of connections between seemingly unrelated phenomena. While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a flow of influence between electoral choices in South Dakota and the hydropower energy landscape in El Salvador.

In this electrifying study, we not only shed light on this unexpected connection but also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics and energize your understanding of interconnected global phenomena.

[[INTRDUCTION]]
The world of political science and energy economics is often a whirlpool of data, theories, and unexpected relationships. In this hydro-politically charged study, we dive headfirst into the curious case of the connection between votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. As researchers, we are often encouraged to "think outside the box," and in this case, we took it quite literally, venturing beyond national borders and across disciplines to uncover this surprising correlation.
The journey to unravel this hydro-political mystery began with the realization that while South Dakota may be famously known for Mount Rushmore, pheasant hunting, and, of course, its strong support for the agricultural sector, its influence may extend beyond state lines and into the hydropower energy landscape of El Salvador. The intricate web of connections between seemingly unrelated phenomena often leaves researchers in a state of shock, akin to electrical overload.
Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embarked on a wild and watery adventure to determine if there's more than meets the eye in this unexpected political and energy dance. The data flowed like a mighty river, guiding us through statistical eddies and political rip currents, ultimately leading to the beacon of correlation.
With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. The statistical significance of this relationship practically shouted at us like a waterfall in a serene forest, impossible to ignore. It's as if the data itself whispered, "Let me be your current guide to unveil the hydro-political connection."
While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a certain synergy, a certain ebb and flow, between electoral choices in South Dakota and the hydropower energy landscape in El Salvador. This unexpected relationship between political preference and energy generation sent ripples through our understanding of global interconnectedness, leaving us in awe of the unseen influences shaping our world.
In this electrifying study, we not only shed light on this unexpected connection – we also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics, and together, let's energize our understanding of the interconnected global phenomena that power the world – both literally and metaphorically.

[[RESULTS]]
The results of our hydro-politically charged study revealed a surprisingly robust correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. With a correlation coefficient of 0.8053368 and an r-squared of 0.6485673, it's safe to say that this connection is no mere drop in the bucket. The p-value of less than 0.01 further solidifies the statistical significance of this relationship, much like how a dam fortifies a river.
To visually capture this striking relationship, we present the scatterplot in Figure 1. This plot elegantly showcases the strong positive correlation between the two variables, almost like a synchronized dance between South Dakota's voting preferences and El Salvador's hydropower energy generation. It's as if these variables are performing a watery waltz across national boundaries, defying conventional expectations with their harmonious rhythm.
The strength of this correlation has left us feeling like we've stumbled upon an unexpected treasure, reminiscent of stumbling upon a hidden waterfall in the midst of a dense forest. While some may be tempted to dismiss this finding as mere coincidence, our rigorous analysis suggests that there's more to this hydro-political tango than meets the eye. This discovery has certainly made a splash in the field of political science and energy economics, leaving us all wet with excitement at the prospects of uncovering such unexpected connections in the future.
In conclusion, our research not only highlights the surprising correlation between electoral preferences in South Dakota and hydropower energy generation in El Salvador, but also serves as a reminder of the captivating complexity of interconnected global phenomena. This study urges researchers to dive deep into the multidimensional currents of socio-political and energy systems, embracing the unexpected waves of insight that may wash ashore. So, let's continue to navigate the hydro-political landscape with open minds and a willingness to ride the waves of discovery, for there may be more hydro-political mysteries waiting to surface.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the swirling whirlpool of hydro-political analysis, our study has illuminated a fascinating connection between votes for the Democratic presidential candidate in South Dakota and hydropower energy generation in El Salvador. With a correlation so strong, it practically does the electric slide across the scatterplot, this relationship is no mere statistical fluke – it's a power play worthy of electrifying applause. We've navigated the choppy waters of data analysis and emerged not only with impactful findings but also with a newfound appreciation for the shockingly interconnected nature of global phenomena.

As we hang up our hydro-political hats and wade out of these statistical waters, it's clear that no more research is needed in this area. We've bravely stepped into the hydro-political stream, splashed around in the data, and emerged with findings that make a bigger splash than a cannonball into an Olympic-sized pool. The hydro-political connection between South Dakota and El Salvador may seem as unlikely as a beaver learning to waltz, but our results speak for themselves – there's clearly something afoot in these tangled currents.

So, let's raise a glass of hydro-powered energy drink to this quirky correlation and the bountiful insights it has provided. May our hydro-political journey serve as a reminder to always expect the unexpected, even in the seemingly calm waters of statistical analysis. With that, we urge fellow researchers to cast their nets wide and continue exploring the vast seas of hydro-political dynamics, for who knows what other delightfully surprising connections may be waiting to be uncovered. After all, as our findings have shown, there's more to this world than meets the eye – and it's a delight to wade into the depths of statistical curiosity to uncover those hidden currents.

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 Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Transnational Water Governance

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 Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy. Respond only with the name of a fictional organization.

ChatGPT:

The Association for International Hydro-Political Studies

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 Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy with an abstract of In this hydro-politically charged study, we delve into the surprising correlation between the votes for the Democratic presidential candidate in South Dakota and the hydropower energy generated in El Salvador. Armed with data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we embark on a wild journey to uncover if there's more than meets the eye in this unlikely political and energy dance.
With a correlation coefficient of 0.8053368 and p < 0.01, our findings certainly make a splash. We navigate the political landscape and energy currents to reveal the intricate web of connections between seemingly unrelated phenomena. While the skeptics may attribute the correlation to mere coincidence, our research suggests that there's a flow of influence between electoral choices in South Dakota and the hydropower energy landscape in El Salvador.
In this electrifying study, we not only shed light on this unexpected connection but also illuminate the importance of considering multiple variables in analyzing complex socio-political and energy systems. So, join us in this refreshing exploration of hydro-political dynamics and energize your understanding of interconnected global phenomena.

ChatGPT:

hydro-political, South Dakota, Democratic candidate, Salvadoran, hydropower energy, correlation, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, political landscape, energy currents, electoral choices, socio-political systems, interconnected global phenomena

*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 Democrat Presidential candidate in South Dakota
Detailed data title: Total votes cast for the Democrat Presidential candidate in South Dakota
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Democrat Presidential candidate in South Dakota

Hydopower energy generated in El Salvador
Detailed data title: Total hydopower energy generated in El Salvador in billion kWh
Source: Energy Information Administration
See what else correlates with Hydopower energy generated in El Salvador

Correlation r = 0.8053368 (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 = 0.6485673 (Coefficient of determination)
This means 64.9% of the change in the one variable (i.e., Hydopower energy generated in El Salvador) is predictable based on the change in the other (i.e., Votes for the Democrat Presidential candidate in South Dakota) over the 11 years from 1980 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0028.^Show 0.0027767903006478685000000000
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 0.81 in 0.28% of random cases. Said differently, if you correlated 360 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.

[ 0.4, 0.95 ] 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.

	1980	1984	1988	1992	1996	2000	2004	2008	2012	2016	2020
Votes for the Democrat Presidential candidate in South Dakota (Total votes)	103855	116113	145560	124888	139333	118804	149244	170924	145039	117458	150471
Hydopower energy generated in El Salvador (Billion kWh)	1.067	1.038	1.31	0.973	1.864	1.163	1.374	2.033	1.842	1.257	1.99

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([103855,116113,145560,124888,139333,118804,149244,170924,145039,117458,150471,])
array_2 = np.array([1.067,1.038,1.31,0.973,1.864,1.163,1.374,2.033,1.842,1.257,1.99,])
array_1_name = "Votes for the Democrat Presidential candidate in South Dakota"
array_2_name = "Hydopower energy generated in El Salvador"

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Votes for the Democrat Presidential candidate in South Dakota
Line chart for only Hydopower energy generated in El Salvador
AI-generated correlation image
The spurious research paper: Hydro-political Connections: The South Dakotan Vote and Salvadoran Hydropower Energy

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Correlation ID: 4965 · Black Variable ID: 26030 · Red Variable ID: 23647


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