Spurious correlation #4,826 · 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 Republican Presidential candidate in Tennessee and the second variable is Biomass power generated in Taiwan. The chart goes from 1989 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 Republican Presidential candidate in Tennessee and the second variable is Biomass power generated in Taiwan. The chart goes from 1989 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The Republicans in Tennessee must have been feeling fired up, leading to a surge in political energy. And as we all know, political energy knows no boundaries, so it somehow sparked a chain reaction all the way to Taiwan's biomass power production. It's like the butterfly effect, but with elephants and pandas thrown into the mix.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a futuristic, high-tech cityscape in Tennessee, with a long line of enthusiastic voters outside the polling stations. The scene should show the "Republican" button being pressed repeatedly, with the voters' vibrant energy transforming into a red wave that shoots across the ocean to Taiwan. In Taiwan, the energy ignites the biomass power plants, visually depicting them rapidly generating electricity in response to the energized Republican voters in Tennessee. The high-tech city and power plants should exude a sleek and dynamic feel, showcasing the powerful connection between political energy and efficient energy production. 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 Tennessee and Biomass power generated in Taiwan. 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: Cybernetic art High-tech Cutsy graphics Minimalism Macabre
ChatGPT: High-tech

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Votes for the Republican Presidential candidate in Tennessee and Biomass power generated in Taiwan, in the following situation: The Republicans in Tennessee must have been feeling fired up, leading to a surge in political energy. And as we all know, political energy knows no boundaries, so it somehow sparked a chain reaction all the way to Taiwan's biomass power production. It's like the butterfly effect, but with elephants and pandas thrown into the mix.. Keep the description simple.
ChatGPT: In Tennessee, a line of enthusiastic voters forms outside the polling stations, with the "Republican" button being pressed repeatedly. As the political energy builds, it radiates outward, transforming into a vibrant red wave that travels across the ocean to Taiwan. In Taiwan, this energy ignites the biomass power plants, causing them to generate electricity at an accelerated rate. The image shows the powerful energy of Republican voters in Tennessee directly fueling the efficient production of biomass power in Taiwan.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ High-tech ] and depicting this scene: In Tennessee, a line of enthusiastic voters forms outside the polling stations, with the "Republican" button being pressed repeatedly. As the political energy builds, it radiates outward, transforming into a vibrant red wave that travels across the ocean to Taiwan. In Taiwan, this energy ignites the biomass power plants, causing them to generate electricity at an accelerated rate. The image shows the powerful energy of Republican voters in Tennessee directly fueling the efficient production of biomass power in Taiwan. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a futuristic, high-tech cityscape in Tennessee, with a long line of enthusiastic voters outside the polling stations. The scene should show the "Republican" button being pressed repeatedly, with the voters' vibrant energy transforming into a red wave that shoots across the ocean to Taiwan. In Taiwan, the energy ignites the biomass power plants, visually depicting them rapidly generating electricity in response to the energized Republican voters in Tennessee. The high-tech city and power plants should exude a sleek and dynamic feel, showcasing the powerful connection between political energy and efficient energy production.

*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 Republican Presidential candidate in Tennessee caused Biomass power generated in Taiwan to increase.

AI academic paper

(Because p < 0.01)

Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan

The Journal of Biomass and Ballots

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Votes for the Republican Presidential candidate in Tennessee and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
"Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan"

[[ABSTRACT]]
As we delve into the intricate interplay of electoral patterns and energy production, we uncover surprising connections between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. Our research, utilizing data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, aims to shed light on this unexplored correlation and leaves no tree unturned.

In our analysis spanning the years 1989 to 2020, we applied rigorous statistical methods and unearthed a remarkable correlation coefficient of 0.9891568 and p < 0.01 between the two seemingly disparate variables. This finding suggests a strong association, unravelling a root-level connection that may sprout unexpected implications.

One might wonder what a Tennessee's Republican preferences have to do with biomass power in Taiwan. Perhaps it's a case of "biomass appeal" or a "political power plant" - pun intended. This unanticipated alignment prompts us to explore potential underlying factors, raising questions as to whether policy decisions, economic influences, or even cultural dynamics play a role in this intriguing relationship.

Our results present a branching new avenue for future research and policy considerations. As we continue to cultivate our understanding of these intricate connections, we recognize that the world of data analysis can truly "leaf" us with unexpected findings – and perhaps some unexpectedly good dad jokes as well.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of politics and energy production is a fertile ground for uncovering unexpected connections. As we dig deep into the data, we are often met with surprising correlations that can "branch" out into unforeseen areas. In this study, we turn our attention to the relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. It's a peculiar pairing, to say the least, but as they say, sometimes the most fruitful discoveries come from the most unexpected places.
Now, one might ask, "What does Republican support in Tennessee have to do with biomass power in Taiwan?" Well, as researchers, it's our job to "log" the data and "root" out the underlying patterns, even if it leads us down unexpected avenues. A little like trying to find the "trunk" of the matter, isn't it?
The surprising connection we have unearthed between these seemingly unrelated variables raises eyebrows and invokes "branch" memories of the old adage, "As the biomass grows, so does the political stance." Oh, the joys of delving into the world of ecological and electoral intrigue.
Our investigation takes us on a journey through the intricate labyrinth of data analysis, where we strive to "leaf" no stone unturned in uncovering the underlying forces at play. We are reminded that in the pursuit of knowledge, sometimes we must be willing to "branch" out from the familiar and embrace the unexpected, much like stumbling upon a dad joke in an academic paper.
As we delve into the details of our findings, we ask ourselves, "Are we barking up the right tree with this correlation?" and we can't help but appreciate the irony of "political lumberjacks" influencing energy decisions across oceans. It's a fascinating insight into the interconnectedness of global systems, and it surely "wood" be remiss of us not to explore its implications further.
In the following sections, we will delve into the methodology and results of our analysis, aiming to provide a comprehensive understanding of this intriguing correlation and perhaps provide a few more "stumped" puns along the way.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The study of unexpected connections has always been a topic of fascination in both academic and casual settings. From Smith's pioneering work on "Political Arboreal Relationships" to Doe's seminal paper "Eco-Political Nexus: Uncovering the Foliage of Electoral Dynamics," scholars have sought to untangle the intricate web of relationships that exist between seemingly disparate variables.

But let's branch out from the serious stuff for a moment and talk about some real "page-turners" that shed light on the topic at hand. In "The Hidden Life of Trees" by Peter Wohlleben, the author explores the interconnectedness of forest ecosystems, providing a metaphorical backdrop for our own exploration of interconnected electoral and energy landscapes. Meanwhile, "The Power Broker" by Robert Caro offers a deep dive into the political dynamics shaping New York City, reminding us that political influence can indeed have far-reaching effects, much like the unexpected link we've stumbled upon.

On the more fictional side, works like "Roots" by Alex Haley and "The Overstory" by Richard Powers might not focus on elections or energy directly, but their exploration of interconnectedness and far-reaching influence certainly resonates with our own findings. And who can forget Dr. Seuss's "The Lorax," a whimsical tale that serves as a cautionary reminder of environmental stewardship and the unexpected consequences of political decisions. After all, in the world of data analysis, even the most unexpected sources can offer valuable insight – never underestimate the power of a good children's story.

As we "vote" on what sources to include in our review, it's essential to recognize the diverse perspectives that can inform our understanding of this surprising correlation. This "biomass-terpiece" of interconnectedness not only challenges our expectations but also provides fertile ground for future research and discovery. And if we find ourselves feeling stumped by the complexity of it all, well, that's just another reminder of the unexpected whimsy of academic exploration.

Now, as we "log off" from the world of literature and step back into the realm of empirical analysis, let's not forget to "branch" out from convention and embrace the unexpected – after all, that's where the most interesting findings often take root.

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 Republican Presidential candidate in Tennessee and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration . You used data from 1989 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]]
"Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan"

[[ABSTRACT]]
As we delve into the intricate interplay of electoral patterns and energy production, we uncover surprising connections between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. Our research, utilizing data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, aims to shed light on this unexplored correlation and leaves no tree unturned.

In our analysis spanning the years 1989 to 2020, we applied rigorous statistical methods and unearthed a remarkable correlation coefficient of 0.9891568 and p < 0.01 between the two seemingly disparate variables. This finding suggests a strong association, unravelling a root-level connection that may sprout unexpected implications.

One might wonder what a Tennessee's Republican preferences have to do with biomass power in Taiwan. Perhaps it's a case of "biomass appeal" or a "political power plant" - pun intended. This unanticipated alignment prompts us to explore potential underlying factors, raising questions as to whether policy decisions, economic influences, or even cultural dynamics play a role in this intriguing relationship.

Our results present a branching new avenue for future research and policy considerations. As we continue to cultivate our understanding of these intricate connections, we recognize that the world of data analysis can truly "leaf" us with unexpected findings – and perhaps some unexpectedly good dad jokes as well.

[[INTRODUCTION]]
The intersection of politics and energy production is a fertile ground for uncovering unexpected connections. As we dig deep into the data, we are often met with surprising correlations that can "branch" out into unforeseen areas. In this study, we turn our attention to the relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. It's a peculiar pairing, to say the least, but as they say, sometimes the most fruitful discoveries come from the most unexpected places.
Now, one might ask, "What does Republican support in Tennessee have to do with biomass power in Taiwan?" Well, as researchers, it's our job to "log" the data and "root" out the underlying patterns, even if it leads us down unexpected avenues. A little like trying to find the "trunk" of the matter, isn't it?
The surprising connection we have unearthed between these seemingly unrelated variables raises eyebrows and invokes "branch" memories of the old adage, "As the biomass grows, so does the political stance." Oh, the joys of delving into the world of ecological and electoral intrigue.
Our investigation takes us on a journey through the intricate labyrinth of data analysis, where we strive to "leaf" no stone unturned in uncovering the underlying forces at play. We are reminded that in the pursuit of knowledge, sometimes we must be willing to "branch" out from the familiar and embrace the unexpected, much like stumbling upon a dad joke in an academic paper.
As we delve into the details of our findings, we ask ourselves, "Are we barking up the right tree with this correlation?" and we can't help but appreciate the irony of "political lumberjacks" influencing energy decisions across oceans. It's a fascinating insight into the interconnectedness of global systems, and it surely "wood" be remiss of us not to explore its implications further.
In the following sections, we will delve into the methodology and results of our analysis, aiming to provide a comprehensive understanding of this intriguing correlation and perhaps provide a few more "stumped" puns along the way.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unearth the connection between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan, our research team employed a series of rigorous and, dare I say, "pulpy" methodologies. We gathered data from reputable sources including the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, ensuring a comprehensive and "tree-mendous" dataset spanning the years 1989 to 2020.

Firstly, we conducted a thorough analysis of the voting patterns in Tennessee, sifting through historical electoral data like detectives searching for clues in a political mystery novel. Our team was like a group of "vote-arborists," pruning away irrelevant data to reveal the core of Republican support in the state. We employed statistical techniques to quantify the magnitude and distribution of these votes, ensuring that no "vote-stump" was left unaccounted for.

Next, our focus shifted to the realm of biomass power generation in Taiwan. In a manner befitting true researchers, we dug deep into the energy production landscape, gathering data on biomass power plants like explorers discovering hidden treasure. We meticulously examined the output, efficiency, and growth of biomass energy in Taiwan, much like "energy botanists" examining the growth rings of a tree.

Once the relevant data was assembled, we set about the task of establishing a connection between these disparate variables. Employing advanced statistical analyses, including correlation tests and regression models, we aimed to untangle the roots of this unexpected relationship. It was akin to charting a map through the "electoral jungle" and the "biomass rainforest," seeking a path that intertwined these seemingly unrelated terrains.

Furthermore, we conducted sensitivity analyses and control tests to ensure the robustness of our findings. As "data gardeners," we tended to our statistical "garden," checking for any weeds of confounding variables that could distort our results. This process allowed us to cultivate a clearer understanding of the "seeds of correlation" between Tennessee's Republican votes and Taiwan's biomass power generation.

Lastly, we employed spatial analysis techniques to explore the geographical aspects of this correlation. Mapping the spatial distribution of Republican support in Tennessee and the location of biomass power plants in Taiwan enabled us to visualize the "political forest" and the "energy ecosystem" in a manner that shed light on the geographic nuances of this unexpected relationship.

The methodology section is the roots of our research, grounding our study in a robust and "tree-mendous" framework that allows our findings to "branch" out into meaningful insights. In the following section, we will delve into the "fruits" of our labor and present the compelling results of our analysis, ripe for the picking and perhaps sprinkled with a few more unexpected puns along the way.

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 Republican Presidential candidate in Tennessee and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

For the time period 1989 to 2020, you found a correlation 0.9891568, r-squared of 0.9784312, 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]]
"Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan"

[[ABSTRACT]]
As we delve into the intricate interplay of electoral patterns and energy production, we uncover surprising connections between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. Our research, utilizing data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, aims to shed light on this unexplored correlation and leaves no tree unturned.

In our analysis spanning the years 1989 to 2020, we applied rigorous statistical methods and unearthed a remarkable correlation coefficient of 0.9891568 and p < 0.01 between the two seemingly disparate variables. This finding suggests a strong association, unravelling a root-level connection that may sprout unexpected implications.

One might wonder what a Tennessee's Republican preferences have to do with biomass power in Taiwan. Perhaps it's a case of "biomass appeal" or a "political power plant" - pun intended. This unanticipated alignment prompts us to explore potential underlying factors, raising questions as to whether policy decisions, economic influences, or even cultural dynamics play a role in this intriguing relationship.

Our results present a branching new avenue for future research and policy considerations. As we continue to cultivate our understanding of these intricate connections, we recognize that the world of data analysis can truly "leaf" us with unexpected findings – and perhaps some unexpectedly good dad jokes as well.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of politics and energy production is a fertile ground for uncovering unexpected connections. As we dig deep into the data, we are often met with surprising correlations that can "branch" out into unforeseen areas. In this study, we turn our attention to the relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. It's a peculiar pairing, to say the least, but as they say, sometimes the most fruitful discoveries come from the most unexpected places.
Now, one might ask, "What does Republican support in Tennessee have to do with biomass power in Taiwan?" Well, as researchers, it's our job to "log" the data and "root" out the underlying patterns, even if it leads us down unexpected avenues. A little like trying to find the "trunk" of the matter, isn't it?
The surprising connection we have unearthed between these seemingly unrelated variables raises eyebrows and invokes "branch" memories of the old adage, "As the biomass grows, so does the political stance." Oh, the joys of delving into the world of ecological and electoral intrigue.
Our investigation takes us on a journey through the intricate labyrinth of data analysis, where we strive to "leaf" no stone unturned in uncovering the underlying forces at play. We are reminded that in the pursuit of knowledge, sometimes we must be willing to "branch" out from the familiar and embrace the unexpected, much like stumbling upon a dad joke in an academic paper.
As we delve into the details of our findings, we ask ourselves, "Are we barking up the right tree with this correlation?" and we can't help but appreciate the irony of "political lumberjacks" influencing energy decisions across oceans. It's a fascinating insight into the interconnectedness of global systems, and it surely "wood" be remiss of us not to explore its implications further.
In the following sections, we will delve into the methodology and results of our analysis, aiming to provide a comprehensive understanding of this intriguing correlation and perhaps provide a few more "stumped" puns along the way.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The analysis of the data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration revealed a remarkably high correlation coefficient of 0.9891568 between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan from 1989 to 2020. This strong correlation suggests a surprising link between these two seemingly unrelated variables, adding a new layer of complexity to the interconnected web of global influences.

Fig. 1 displays a scatterplot illustrating the robust relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan, providing a visual representation of the significant correlation unearthed in our analysis. It's a real "tree-t" to behold, if you'll pardon the pun.

The r-squared value of 0.9784312 further reinforces the strength of this correlation, indicating that the Republican support in Tennessee explains approximately 97.84% of the variability in Biomass power generation in Taiwan. Now, that's what we call some powerful political and ecological synergy. One could say it's a "bushy" situation, couldn't they?

With a p-value of less than 0.01, our findings hold statistical significance, highlighting the robustness and reliability of the observed correlation. It seems that this unexpected connection blossoms with significance, much like a particularly convincing political stance or a particularly effective power source.

Our results suggest that beneath the surface, there may be underlying factors intertwining these two variables, indicating that policy decisions, economic influences, or even cultural dynamics could be influencing the relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. It appears that this correlation has deep roots indeed.

This unexpected correlation between seemingly distant geopolitical factors could inspire a myriad of future investigations. The unanticipated alignment of these variables branches out into unexplored territories of inquiry, offering fertile ground for scholars and policymakers to explore further. It seems that political preferences and energy production may be more intertwined than we initially "barked" up to be – pun absolutely intended.

As we aim to deepen our comprehension of these surprising connections, we must remain vigilant for potential blind spots and biases. It's essential to recognize that the interplay between electoral patterns and energy production can lead us to unexpected discoveries, and perhaps a few unexpected laughs along the way.

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 Republican Presidential candidate in Tennessee and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan"

[[ABSTRACT]]
As we delve into the intricate interplay of electoral patterns and energy production, we uncover surprising connections between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. Our research, utilizing data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, aims to shed light on this unexplored correlation and leaves no tree unturned.

In our analysis spanning the years 1989 to 2020, we applied rigorous statistical methods and unearthed a remarkable correlation coefficient of 0.9891568 and p < 0.01 between the two seemingly disparate variables. This finding suggests a strong association, unravelling a root-level connection that may sprout unexpected implications.

One might wonder what a Tennessee's Republican preferences have to do with biomass power in Taiwan. Perhaps it's a case of "biomass appeal" or a "political power plant" - pun intended. This unanticipated alignment prompts us to explore potential underlying factors, raising questions as to whether policy decisions, economic influences, or even cultural dynamics play a role in this intriguing relationship.

Our results present a branching new avenue for future research and policy considerations. As we continue to cultivate our understanding of these intricate connections, we recognize that the world of data analysis can truly "leaf" us with unexpected findings – and perhaps some unexpectedly good dad jokes as well.

[[LITERATURE REVIEW]]
The study of unexpected connections has always been a topic of fascination in both academic and casual settings. From Smith's pioneering work on "Political Arboreal Relationships" to Doe's seminal paper "Eco-Political Nexus: Uncovering the Foliage of Electoral Dynamics," scholars have sought to untangle the intricate web of relationships that exist between seemingly disparate variables.
But let's branch out from the serious stuff for a moment and talk about some real "page-turners" that shed light on the topic at hand. In "The Hidden Life of Trees" by Peter Wohlleben, the author explores the interconnectedness of forest ecosystems, providing a metaphorical backdrop for our own exploration of interconnected electoral and energy landscapes. Meanwhile, "The Power Broker" by Robert Caro offers a deep dive into the political dynamics shaping New York City, reminding us that political influence can indeed have far-reaching effects, much like the unexpected link we've stumbled upon.
On the more fictional side, works like "Roots" by Alex Haley and "The Overstory" by Richard Powers might not focus on elections or energy directly, but their exploration of interconnectedness and far-reaching influence certainly resonates with our own findings. And who can forget Dr. Seuss's "The Lorax," a whimsical tale that serves as a cautionary reminder of environmental stewardship and the unexpected consequences of political decisions. After all, in the world of data analysis, even the most unexpected sources can offer valuable insight – never underestimate the power of a good children's story.
As we "vote" on what sources to include in our review, it's essential to recognize the diverse perspectives that can inform our understanding of this surprising correlation. This "biomass-terpiece" of interconnectedness not only challenges our expectations but also provides fertile ground for future research and discovery. And if we find ourselves feeling stumped by the complexity of it all, well, that's just another reminder of the unexpected whimsy of academic exploration.
Now, as we "log off" from the world of literature and step back into the realm of empirical analysis, let's not forget to "branch" out from convention and embrace the unexpected – after all, that's where the most interesting findings often take root.

[[RESULTS]]
The analysis of the data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration revealed a remarkably high correlation coefficient of 0.9891568 between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan from 1989 to 2020. This strong correlation suggests a surprising link between these two seemingly unrelated variables, adding a new layer of complexity to the interconnected web of global influences.
Fig. 1 displays a scatterplot illustrating the robust relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan, providing a visual representation of the significant correlation unearthed in our analysis. It's a real "tree-t" to behold, if you'll pardon the pun.
The r-squared value of 0.9784312 further reinforces the strength of this correlation, indicating that the Republican support in Tennessee explains approximately 97.84% of the variability in Biomass power generation in Taiwan. Now, that's what we call some powerful political and ecological synergy. One could say it's a "bushy" situation, couldn't they?
With a p-value of less than 0.01, our findings hold statistical significance, highlighting the robustness and reliability of the observed correlation. It seems that this unexpected connection blossoms with significance, much like a particularly convincing political stance or a particularly effective power source.
Our results suggest that beneath the surface, there may be underlying factors intertwining these two variables, indicating that policy decisions, economic influences, or even cultural dynamics could be influencing the relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. It appears that this correlation has deep roots indeed.
This unexpected correlation between seemingly distant geopolitical factors could inspire a myriad of future investigations. The unanticipated alignment of these variables branches out into unexplored territories of inquiry, offering fertile ground for scholars and policymakers to explore further. It seems that political preferences and energy production may be more intertwined than we initially "barked" up to be – pun absolutely intended.
As we aim to deepen our comprehension of these surprising connections, we must remain vigilant for potential blind spots and biases. It's essential to recognize that the interplay between electoral patterns and energy production can lead us to unexpected discoveries, and perhaps a few unexpected laughs along the way.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings provide compelling evidence of a strong and statistically significant correlation between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan, bolstering prior research that has probed the role of unexpected and tangentially related factors in shaping complex global dynamics. By shedding light on this surprising connection, our study adds a unique branch to the growing body of literature on electoral and environmental influences.

Our results align with the pioneering work of Smith and Doe, demonstrating that seemingly disparate variables can indeed exhibit substantial correlations. While our study may have cultivated this connection in a lighthearted fashion, the robust statistical support underpinning this correlation serves as a firm reminder of the unexpected underpinnings that can shape our world. As much as we may leaf through the literature and data with a sense of whimsy, the evidence supporting this correlation truly takes root in the realm of empirical rigour.

The high correlation coefficient we unearthed is not only eyebrow-raising but also scientifically significant, indicating a strong interdependence between political leanings in Tennessee and biomass power generation in Taiwan. This finding echoes the sentiment conveyed in "The Hidden Life of Trees" - much like the intricate interconnections within a forest ecosystem, our research reveals the entwined nature of seemingly unrelated geopolitical and environmental phenomena.

The robustness of the correlation, as reflected in the high r-squared value, signifies that almost 98% of the variance in Biomass power generation in Taiwan can be explained by the voting patterns in Tennessee. This result underscores the potency of political influence in reverberating across national borders, much like a mighty oak spreading its roots far and wide.

Our findings offer stimulating fodder for future inquiries, expanding the horizon of research into the confluence of political dynamics and renewable energy sources. The unexpected alignment of these variables branches out into unexplored territories of interdisciplinary collaboration, beckoning scholars and policymakers to delve deeper into the intersection of political preferences and energy production.

In a field often characterized by solemn analyses and dry discourse, our study injects a dash of humor and levity, reminding the academic community that even the most unexpected connections can bloom into substantive findings. From "biomass appeal" to "political power plants," our research, while rooted in rigorous methodology, doesn't shy away from embracing the unexpected whimsy that can sprout from empirical investigation.

As we continue to nurture our understanding of these unexpected connections, we must remain open to the possibility of discovering new and unconventional relationships in the vast and complex landscape of global influences. After all, in the world of data analysis, unexpected surprises may just be as common as a pun in a dad's joke repertoire.

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 Republican Presidential candidate in Tennessee and Biomass power generated in Taiwan. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan"

[[ABSTRACT]]
As we delve into the intricate interplay of electoral patterns and energy production, we uncover surprising connections between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. Our research, utilizing data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, aims to shed light on this unexplored correlation and leaves no tree unturned.

In our analysis spanning the years 1989 to 2020, we applied rigorous statistical methods and unearthed a remarkable correlation coefficient of 0.9891568 and p < 0.01 between the two seemingly disparate variables. This finding suggests a strong association, unravelling a root-level connection that may sprout unexpected implications.

One might wonder what a Tennessee's Republican preferences have to do with biomass power in Taiwan. Perhaps it's a case of "biomass appeal" or a "political power plant" - pun intended. This unanticipated alignment prompts us to explore potential underlying factors, raising questions as to whether policy decisions, economic influences, or even cultural dynamics play a role in this intriguing relationship.

Our results present a branching new avenue for future research and policy considerations. As we continue to cultivate our understanding of these intricate connections, we recognize that the world of data analysis can truly "leaf" us with unexpected findings – and perhaps some unexpectedly good dad jokes as well.

[[INTRDUCTION]]
The intersection of politics and energy production is a fertile ground for uncovering unexpected connections. As we dig deep into the data, we are often met with surprising correlations that can "branch" out into unforeseen areas. In this study, we turn our attention to the relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. It's a peculiar pairing, to say the least, but as they say, sometimes the most fruitful discoveries come from the most unexpected places.
Now, one might ask, "What does Republican support in Tennessee have to do with biomass power in Taiwan?" Well, as researchers, it's our job to "log" the data and "root" out the underlying patterns, even if it leads us down unexpected avenues. A little like trying to find the "trunk" of the matter, isn't it?
The surprising connection we have unearthed between these seemingly unrelated variables raises eyebrows and invokes "branch" memories of the old adage, "As the biomass grows, so does the political stance." Oh, the joys of delving into the world of ecological and electoral intrigue.
Our investigation takes us on a journey through the intricate labyrinth of data analysis, where we strive to "leaf" no stone unturned in uncovering the underlying forces at play. We are reminded that in the pursuit of knowledge, sometimes we must be willing to "branch" out from the familiar and embrace the unexpected, much like stumbling upon a dad joke in an academic paper.
As we delve into the details of our findings, we ask ourselves, "Are we barking up the right tree with this correlation?" and we can't help but appreciate the irony of "political lumberjacks" influencing energy decisions across oceans. It's a fascinating insight into the interconnectedness of global systems, and it surely "wood" be remiss of us not to explore its implications further.
In the following sections, we will delve into the methodology and results of our analysis, aiming to provide a comprehensive understanding of this intriguing correlation and perhaps provide a few more "stumped" puns along the way.

[[RESULTS]]
The analysis of the data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration revealed a remarkably high correlation coefficient of 0.9891568 between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan from 1989 to 2020. This strong correlation suggests a surprising link between these two seemingly unrelated variables, adding a new layer of complexity to the interconnected web of global influences.
Fig. 1 displays a scatterplot illustrating the robust relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan, providing a visual representation of the significant correlation unearthed in our analysis. It's a real "tree-t" to behold, if you'll pardon the pun.
The r-squared value of 0.9784312 further reinforces the strength of this correlation, indicating that the Republican support in Tennessee explains approximately 97.84% of the variability in Biomass power generation in Taiwan. Now, that's what we call some powerful political and ecological synergy. One could say it's a "bushy" situation, couldn't they?
With a p-value of less than 0.01, our findings hold statistical significance, highlighting the robustness and reliability of the observed correlation. It seems that this unexpected connection blossoms with significance, much like a particularly convincing political stance or a particularly effective power source.
Our results suggest that beneath the surface, there may be underlying factors intertwining these two variables, indicating that policy decisions, economic influences, or even cultural dynamics could be influencing the relationship between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. It appears that this correlation has deep roots indeed.
This unexpected correlation between seemingly distant geopolitical factors could inspire a myriad of future investigations. The unanticipated alignment of these variables branches out into unexplored territories of inquiry, offering fertile ground for scholars and policymakers to explore further. It seems that political preferences and energy production may be more intertwined than we initially "barked" up to be – pun absolutely intended.
As we aim to deepen our comprehension of these surprising connections, we must remain vigilant for potential blind spots and biases. It's essential to recognize that the interplay between electoral patterns and energy production can lead us to unexpected discoveries, and perhaps a few unexpected laughs along the way.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unveiled a remarkable correlation between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan, proving that in the tangled web of global influences, even the most unlikely pairings can be rooted in significant relationships. Our findings have "stumped" us in the best possible way, reminding us that the world of data analysis can indeed "leaf" us with unexpected discoveries and punny opportunities.

The robust correlation coefficient and statistical significance indicate that the sway of Republican support in Tennessee on Biomass power generation in Taiwan is not just a mere "trunk"ated phenomenon – it's a real "tree-t" to behold. It seems that political preferences may have a "branching" effect on international energy dynamics, demonstrating the interconnectedness of ecological and electoral landscapes.

As we "branch" out into the implications of these surprising findings, it's clear that this correlation has deep roots worthy of further exploration. However, it's safe to say that we must "leaf" it at that and not "bark" up the wrong tree with more convoluted puns. After all, too many puns can make readers turn "ashen."

In summary, our research encourages future investigations into the underlying mechanisms driving this unexpected connection while prompting a reevaluation of the interconnected systems at play. However, it seems that no further research is needed to confirm the existence of a surprising correlation between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. It's a "branch" of scientific inquiry we can prune from the list.

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 Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Biomass and Ballots

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 Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Transnational Biomass 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 Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan with an abstract of As we delve into the intricate interplay of electoral patterns and energy production, we uncover surprising connections between Votes for the Republican Presidential candidate in Tennessee and Biomass power generation in Taiwan. Our research, utilizing data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, aims to shed light on this unexplored correlation and leaves no tree unturned.
In our analysis spanning the years 1989 to 2020, we applied rigorous statistical methods and unearthed a remarkable correlation coefficient of 0.9891568 and p < 0.01 between the two seemingly disparate variables. This finding suggests a strong association, unravelling a root-level connection that may sprout unexpected implications.
One might wonder what a Tennessee's Republican preferences have to do with biomass power in Taiwan. Perhaps it's a case of "biomass appeal" or a "political power plant" - pun intended. This unanticipated alignment prompts us to explore potential underlying factors, raising questions as to whether policy decisions, economic influences, or even cultural dynamics play a role in this intriguing relationship.
Our results present a branching new avenue for future research and policy considerations. As we continue to cultivate our understanding of these intricate connections, we recognize that the world of data analysis can truly "leaf" us with unexpected findings – and perhaps some unexpectedly good dad jokes as well.

ChatGPT:

Republican Presidential Candidate Tennessee Votes, Biomass Power Generation Taiwan, Electoral Patterns and Energy Production, Correlation between Voting and Biomass Power, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, Statistical Methods in Data Analysis

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

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Data details

Votes for the Republican Presidential candidate in Tennessee
Detailed data title: Percentage of all votes cast for the Republican Presidential candidate in Tennessee
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Republican Presidential candidate in Tennessee

Biomass power generated in Taiwan
Detailed data title: Total biomass power generated in Taiwan in billion kWh
Source: Energy Information Administration
See what else correlates with Biomass power generated in Taiwan

Correlation r = 0.9891568 (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 = 0.9784312 (Coefficient of determination)
This means 97.8% of the change in the one variable (i.e., Biomass power generated in Taiwan) is predictable based on the change in the other (i.e., Votes for the Republican Presidential candidate in Tennessee) over the 8 years from 1989 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.2E-6.^Show 0.0000031613324120133738000000
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 0.99 in 0.00032% of random cases. Said differently, if you correlated 316,322 random variables^Note You don't actually need 316 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 7 degrees of freedom, ^Note 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.

[ 0.94, 1 ] 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.

	1992	1996	2000	2004	2008	2012	2016	2020
Votes for the Republican Presidential candidate in Tennessee (Percentage of votes)	42.4334	45.5904	51.1492	56.7991	56.897	59.4787	60.722	60.6603
Biomass power generated in Taiwan (Billion kWh)	0.275204	0.690862	1.79272	3.3493	3.36123	3.4192	3.60197	3.75309

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.
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. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([42.4334,45.5904,51.1492,56.7991,56.897,59.4787,60.722,60.6603,])
array_2 = np.array([0.275204,0.690862,1.79272,3.3493,3.36123,3.4192,3.60197,3.75309,])
array_1_name = "Votes for the Republican Presidential candidate in Tennessee"
array_2_name = "Biomass power generated in Taiwan"

# 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 Republican Presidential candidate in Tennessee
Line chart for only Biomass power generated in Taiwan
AI-generated correlation image
The spurious research paper: Branching Out: Unearthing the Electoral Biomass – A Study on the Relationship Between Votes for the Republican Presidential Candidate in Tennessee and Biomass Power Generation in Taiwan

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Correlation ID: 4826 · Black Variable ID: 26171 · Red Variable ID: 24024


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