Spurious correlation #4,452 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in cotton and the second variable is Votes for the Republican Presidential candidate in West Virginia. The chart goes from 2000 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 GMO use in cotton and the second variable is Votes for the Republican Presidential candidate in West Virginia. The chart goes from 2000 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the GMO cotton plants grew larger and stronger, their roots inadvertently tapped into a dormant reservoir of patriotic spirit in the soil, leading to a surge in Republican support. Remember, when it comes to politics, even the fabric of our nation can't help but get involved!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image inspired by the TIME magazine cover style, illustrating an unusual connection between genetically modified cotton plants and politics in West Virginia. The vibrant cotton field is the focal point, with GMO plants standing tall and healthy, their roots delving deep into the rich soil. The patriotism is subtly represented by faint red, white, and blue hues shimmering around the plants, evoking a sense of national pride. In the background, a map of West Virginia depicts a surge of Republican votes, symbolized by a growing number of stars appearing across the state. The unlikely juxtaposition of the thriving GMO cotton plants and the political landscape conveys the unexpected influence of GMO use and the support for the Republican candidate in West Virginia. 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: GMO use in cotton and Votes for the Republican Presidential candidate in West Virginia. 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: Symbolism A TIME magazine cover image Minimalism Cybernetic art Scalar art
ChatGPT: A TIME magazine cover image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both GMO use in cotton and Votes for the Republican Presidential candidate in West Virginia, in the following situation: As the GMO cotton plants grew larger and stronger, their roots inadvertently tapped into a dormant reservoir of patriotic spirit in the soil, leading to a surge in Republican support. Remember, when it comes to politics, even the fabric of our nation can't help but get involved!. Keep the description simple.
ChatGPT: In a vibrant cotton field, the GMO plants stand tall and healthy, with their roots reaching deep into the rich soil. As they absorb nutrients, patriotism seems to emanate from the ground, portrayed by faint red, white, and blue hues shimmering around the plants. In the background, a map of West Virginia shows a surge of Republican votes, symbolized by a growing number of stars appearing across the state. This unlikely connection between cotton and politics showcases the unexpected influence of GMO use and the support for the Republican candidate in West Virginia.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A TIME magazine cover image ] and depicting this scene: In a vibrant cotton field, the GMO plants stand tall and healthy, with their roots reaching deep into the rich soil. As they absorb nutrients, patriotism seems to emanate from the ground, portrayed by faint red, white, and blue hues shimmering around the plants. In the background, a map of West Virginia shows a surge of Republican votes, symbolized by a growing number of stars appearing across the state. This unlikely connection between cotton and politics showcases the unexpected influence of GMO use and the support for the Republican candidate in West Virginia. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image inspired by the TIME magazine cover style, illustrating an unusual connection between genetically modified cotton plants and politics in West Virginia. The vibrant cotton field is the focal point, with GMO plants standing tall and healthy, their roots delving deep into the rich soil. The patriotism is subtly represented by faint red, white, and blue hues shimmering around the plants, evoking a sense of national pride. In the background, a map of West Virginia depicts a surge of Republican votes, symbolized by a growing number of stars appearing across the state. The unlikely juxtaposition of the thriving GMO cotton plants and the political landscape conveys the unexpected influence of GMO use and the support for the Republican candidate in West Virginia.

*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 GMO use in cotton caused Votes for the Republican Presidential candidate in West Virginia to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Seeding Republican Votes: The GMO Connection in West Virginia Politics

The Journal of Political Botany

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 GMO use in cotton and Votes for the Republican Presidential candidate in West Virginia. 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. Then name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
"Seeding Republican Votes: The GMO Connection in West Virginia Politics"

[[ABSTRACT]]
In this study, we delve into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. Armed with data from the USDA and MIT Election Data and Science Lab, as well as the Harvard Dataverse, we set out to uncover whether there's a statistical link between these seemingly unrelated entities. The findings reveal a striking correlation coefficient of 0.9766999 and a p-value of less than 0.01 during the period from 2000 to 2020, begging the question: is there a boll weevil in the voting booth, or have these data simply touched upon the fabric of West Virginian political preference? Join us as we unravel the intertwined threads of GMOs and red votes, and attempt to stitch together a coherent narrative in this agricultural and political quilt of a state.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Introduction
The intersection of agriculture and politics has long been fertile ground for scholarly inquiry. In this paper, we embark on a whimsical journey through the rolling hills and hollers of West Virginia to explore the curious alignment between the use of genetically modified organisms (GMOs) in cotton farming and the voting patterns for the Republican presidential candidate. Like a cotton gin separating fiber from seed, we aim to discern whether there is a clear separation or an intertwining of these two seemingly distinct phenomena.
While West Virginia is renowned for its coal mining heritage, its agricultural sector, particularly in the cultivation of cotton, has quietly spun its own tale. At the same time, the state's political landscape, especially in presidential elections, has often defied national trends. If we may weave a metaphor, West Virginia's political fabric has been woven from red threads, with the state consistently showing robust support for the Republican party.
Straddling the worlds of agriculture and politics, we set out to unravel the Gordian knot between GMO cotton and Republican votes in the Mountain State. Much like genetically engineering a stronger strain of cotton, we have utilized a robust methodology and a trove of data to conduct a rigorous analysis.
The findings that we present in this paper are as surprising as encountering a kernel of popcorn in a bale of cotton. Our statistical analysis, spanning the years 2000 to 2020, has unveiled a correlation coefficient of 0.9766999 and a p-value of less than 0.01, suggesting a remarkably strong association between GMO cotton usage and the propensity to cast a Republican ballot in West Virginia. This correlation is so striking that one might be tempted to call it "corny," but let us resist the pun and proceed with solemnity.
As we delve into the heart of these intriguing findings, we invite you to join us on this expedition through the field of agricultural biotechnology and the labyrinthine corridors of West Virginia politics. Together, we shall sow the seeds of knowledge, cultivate a deeper understanding, and perhaps harvest some unexpected insights from this hybridization of seemingly disparate subjects. So, buckle up and brace yourselves for a journey that promises to be as bountiful as a cornucopia and as twisty as a strand of DNA!

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
Literature Review

The relationship between GMO use in cotton farming and the voting behavior for the Republican presidential candidate in West Virginia has inspired a range of scholarly investigations. Smith (2015) examined the agricultural landscape in West Virginia and its political implications, while Doe (2018) conducted a thorough analysis of GMO adoption in the state. Jones (2019) further explored voting patterns and party preferences in West Virginia. While these studies form the warp and weft of our understanding, there remains ample room for additional exploration and in-depth analysis.

Turning to the broader literature on agricultural biotechnology and its societal impact, "The Impact of Genetically Engineered Crops on Farm Sustainability in the United States" by Fernandez et al. (2014) sheds light on the broader implications of GMO adoption in farming. Similarly, "Biotechnology and the Agricultural Industry" by Brown (2017) offers a comprehensive examination of the economic and environmental dimensions of GMO use in agriculture. These works provide a strong foundation for contextualizing the potential influence of GMO cotton on political behavior in West Virginia.

In a departure from the more traditional academic literature, the fiction works "Cotton Gin and Tonic: A Political Thriller" by Author McAuthorface and "The Boll Weevil Conspiracy" by Mystery Writer X delve into the realm of political intrigue and agricultural drama. Though not rooted in objective data analysis, these narratives showcase the fertile ground for imaginative exploration of the potential entanglement between cotton farming and political machinations.

As part of the researchers' quest to gain a holistic understanding of West Virginia's political landscape, the TV shows "Coal and Cotton: Clash of Clans" and "Mountaineer Politics Unwound" were also explored for insights. While not providing empirical evidence, these shows offered a rich tapestry of anecdotal observations and dramatic depictions of the intersection between agriculture and politics in the state.

With this diverse array of literature at our disposal, we lay the groundwork for our own investigation into the GMO-cotton-Republican-vote nexus, aiming to tease out the strands that may connect these seemingly disparate domains.

Ah, the joys of scholarly inquiry mixed with a dollop of agricultural intrigue and political shenanigans! Let's embark on this comical and captivating journey through the cornfields and ballot boxes of West Virginia.

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 GMO use in cotton and Votes for the Republican Presidential candidate in West Virginia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and MIT Election Data and Science Lab, Harvard Dataverse . You used data from 2000 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]]
"Seeding Republican Votes: The GMO Connection in West Virginia Politics"

[[ABSTRACT]]
In this study, we delve into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. Armed with data from the USDA and MIT Election Data and Science Lab, as well as the Harvard Dataverse, we set out to uncover whether there's a statistical link between these seemingly unrelated entities. The findings reveal a striking correlation coefficient of 0.9766999 and a p-value of less than 0.01 during the period from 2000 to 2020, begging the question: is there a boll weevil in the voting booth, or have these data simply touched upon the fabric of West Virginian political preference? Join us as we unravel the intertwined threads of GMOs and red votes, and attempt to stitch together a coherent narrative in this agricultural and political quilt of a state.

[[INTRODUCTION]]
Introduction
The intersection of agriculture and politics has long been fertile ground for scholarly inquiry. In this paper, we embark on a whimsical journey through the rolling hills and hollers of West Virginia to explore the curious alignment between the use of genetically modified organisms (GMOs) in cotton farming and the voting patterns for the Republican presidential candidate. Like a cotton gin separating fiber from seed, we aim to discern whether there is a clear separation or an intertwining of these two seemingly distinct phenomena.
While West Virginia is renowned for its coal mining heritage, its agricultural sector, particularly in the cultivation of cotton, has quietly spun its own tale. At the same time, the state's political landscape, especially in presidential elections, has often defied national trends. If we may weave a metaphor, West Virginia's political fabric has been woven from red threads, with the state consistently showing robust support for the Republican party.
Straddling the worlds of agriculture and politics, we set out to unravel the Gordian knot between GMO cotton and Republican votes in the Mountain State. Much like genetically engineering a stronger strain of cotton, we have utilized a robust methodology and a trove of data to conduct a rigorous analysis.
The findings that we present in this paper are as surprising as encountering a kernel of popcorn in a bale of cotton. Our statistical analysis, spanning the years 2000 to 2020, has unveiled a correlation coefficient of 0.9766999 and a p-value of less than 0.01, suggesting a remarkably strong association between GMO cotton usage and the propensity to cast a Republican ballot in West Virginia. This correlation is so striking that one might be tempted to call it "corny," but let us resist the pun and proceed with solemnity.
As we delve into the heart of these intriguing findings, we invite you to join us on this expedition through the field of agricultural biotechnology and the labyrinthine corridors of West Virginia politics. Together, we shall sow the seeds of knowledge, cultivate a deeper understanding, and perhaps harvest some unexpected insights from this hybridization of seemingly disparate subjects. So, buckle up and brace yourselves for a journey that promises to be as bountiful as a cornucopia and as twisty as a strand of DNA!

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the enigmatic interplay between genetically modified organisms (GMOs) in cotton farming and the voting behavior of West Virginians in presidential elections, we employed a research methodology that was as carefully curated as a well-tended garden. Our data collection efforts primarily focused on sources such as the United States Department of Agriculture (USDA) and the MIT Election Data and Science Lab, along with the agricultural treasure trove of the Harvard Dataverse. We sought to harvest a comprehensive dataset spanning the years 2000 to 2020, akin to cultivating a bumper crop of information ripe for analysis.

The first step in our methodological odyssey was to plow through the available data on GMO cotton cultivation in West Virginia. We furrowed our brows and diligently combed through agricultural reports, procurement records, and cotton production statistics with the precision of a farmer planting rows of seeds. Our objective was to glean a thorough understanding of the extent and distribution of GMO cotton usage across the state, much like discerning the varied patterns in a sprawling patchwork quilt.

With a rich harvest of agricultural data in our granary, we proceeded to sow the seeds of statistical analysis. Employing a rigorous approach reminiscent of a well-structured crop rotation plan, we calculated descriptive statistics to characterize the distribution of GMO cotton farming in West Virginia. Continuing our methodological gardening, we plowed through the fertile fields of regression analysis to examine the relationship between the prevalence of GMO cotton and the percentage of votes garnered by the Republican presidential candidate.

In this research endeavor, we acknowledged the potential influence of confounding variables, akin to navigating the unpredictability of weather patterns in agricultural endeavors. In order to account for these variables, we employed multivariate regression models, which allowed us to control for factors such as demographic trends, economic indicators, and historical political patterns. This meticulous approach aimed to ensure that our analysis did not yield results as erratic as a wildflower patch in a gusty wind.

Furthermore, to fortify the robustness of our findings, we conducted sensitivity analyses akin to stress-testing the resilience of a crop against adverse weather conditions. These analyses involved varying our model specifications, examining different time periods, and testing alternative measures of GMO cotton usage, ensuring that our conclusions were anchored in the bedrock of methodological scrutiny and not merely floating like a dandelion seed in the breeze.

Finally, having cultivated and nurtured our methodological approach, we reaped the statistical harvest that bore fruit in the form of a correlation coefficient of 0.9766999 and a p-value of less than 0.01. These findings stood as sturdy as a well-trellised grapevine, establishing a robust association between the prevalence of GMO cotton and the propensity to cast Republican votes in West Virginia.

In summary, our methodology, much like tending to a thriving agricultural crop, involved meticulous data collection, careful statistical analysis, and the cultivation of a nuanced understanding of the intertwined threads of GMO cotton and Republican votes in the colorful tapestry of West Virginia politics.

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 GMO use in cotton and Votes for the Republican Presidential candidate in West Virginia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

For the time period 2000 to 2020, you found a correlation 0.9766999, r-squared of 0.9539427, 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]]
"Seeding Republican Votes: The GMO Connection in West Virginia Politics"

[[ABSTRACT]]
In this study, we delve into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. Armed with data from the USDA and MIT Election Data and Science Lab, as well as the Harvard Dataverse, we set out to uncover whether there's a statistical link between these seemingly unrelated entities. The findings reveal a striking correlation coefficient of 0.9766999 and a p-value of less than 0.01 during the period from 2000 to 2020, begging the question: is there a boll weevil in the voting booth, or have these data simply touched upon the fabric of West Virginian political preference? Join us as we unravel the intertwined threads of GMOs and red votes, and attempt to stitch together a coherent narrative in this agricultural and political quilt of a state.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Introduction
The intersection of agriculture and politics has long been fertile ground for scholarly inquiry. In this paper, we embark on a whimsical journey through the rolling hills and hollers of West Virginia to explore the curious alignment between the use of genetically modified organisms (GMOs) in cotton farming and the voting patterns for the Republican presidential candidate. Like a cotton gin separating fiber from seed, we aim to discern whether there is a clear separation or an intertwining of these two seemingly distinct phenomena.
While West Virginia is renowned for its coal mining heritage, its agricultural sector, particularly in the cultivation of cotton, has quietly spun its own tale. At the same time, the state's political landscape, especially in presidential elections, has often defied national trends. If we may weave a metaphor, West Virginia's political fabric has been woven from red threads, with the state consistently showing robust support for the Republican party.
Straddling the worlds of agriculture and politics, we set out to unravel the Gordian knot between GMO cotton and Republican votes in the Mountain State. Much like genetically engineering a stronger strain of cotton, we have utilized a robust methodology and a trove of data to conduct a rigorous analysis.
The findings that we present in this paper are as surprising as encountering a kernel of popcorn in a bale of cotton. Our statistical analysis, spanning the years 2000 to 2020, has unveiled a correlation coefficient of 0.9766999 and a p-value of less than 0.01, suggesting a remarkably strong association between GMO cotton usage and the propensity to cast a Republican ballot in West Virginia. This correlation is so striking that one might be tempted to call it "corny," but let us resist the pun and proceed with solemnity.
As we delve into the heart of these intriguing findings, we invite you to join us on this expedition through the field of agricultural biotechnology and the labyrinthine corridors of West Virginia politics. Together, we shall sow the seeds of knowledge, cultivate a deeper understanding, and perhaps harvest some unexpected insights from this hybridization of seemingly disparate subjects. So, buckle up and brace yourselves for a journey that promises to be as bountiful as a cornucopia and as twisty as a strand of DNA!

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The foundation of our study lies in the analysis of the relationship between the usage of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. After an arduous excavation of data from the USDA, MIT Election Data and Science Lab, and the Harvard Dataverse, we unearthed a remarkable correlation coefficient of 0.9766999, an r-squared value of 0.9539427, and a p-value of less than 0.01 for the time span of 2000 to 2020. These figures indicate a robust and statistically significant relationship between the variables under scrutiny.

Fig. 1 illustrates the near-linear relationship between the two variables, akin to the razor-sharp precision of a cotton gin separating fibers from seeds. It's no cotton candy, folks - the data don't lie. The plot portrays a discernible rising trend, mirroring the steadfast support for the Republican party in West Virginia.

In this statistical landscape, the correlation coefficient of 0.9766999 stands as a towering redwood in the forest of data analysis. This coefficient signifies a nearly perfect positive relationship between the use of GMOs in cotton farming and the inclination to cast a Republican vote. It's a bit like finding a needle in a haystack - a correlation this strong is about as common as a four-leaf clover.

Furthermore, the r-squared value of 0.9539427 underscores that over 95% of the variability in the percentage of Republican votes can be explained by the usage of GMOs in cotton farming. If that doesn't make a compelling case for the influence of genetically modified cotton on politicking, I don't know what does.

The p-value of less than 0.01 adds a cherry on top of this genetically modified sundae. This minuscule p-value indicates that the observed association is highly unlikely to have occurred by mere chance. One might say it's as unlikely as finding a purple cow grazing in a field of white. In other words, the probability of this correlation being a fluke is about as slim as a GMO cotton seed.

In sum, our findings pave the way for further exploration of the intriguing links between agriculture and politics, with West Virginia serving as a ripe field for study. The robust statistical evidence we have presented may leave some scratching their heads, pondering whether we've stumbled upon a boll weevil invasion in the voting booth or simply unraveled the fabric of West Virginian political preference. Join us as we continue to plow through the fertile ground of GMOs and red votes, unraveling the intricate connections that shape the political quilt of the Mountain State.

So, there you have it - a statistical tale as intriguing as a mystery novel and as captivating as a gripping political drama. Let's continue to peel back the layers of this unlikely correlation and see where the threads of GMO cotton and Republican votes lead us next.

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 GMO use in cotton and Votes for the Republican Presidential candidate in West Virginia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Seeding Republican Votes: The GMO Connection in West Virginia Politics"

[[ABSTRACT]]
In this study, we delve into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. Armed with data from the USDA and MIT Election Data and Science Lab, as well as the Harvard Dataverse, we set out to uncover whether there's a statistical link between these seemingly unrelated entities. The findings reveal a striking correlation coefficient of 0.9766999 and a p-value of less than 0.01 during the period from 2000 to 2020, begging the question: is there a boll weevil in the voting booth, or have these data simply touched upon the fabric of West Virginian political preference? Join us as we unravel the intertwined threads of GMOs and red votes, and attempt to stitch together a coherent narrative in this agricultural and political quilt of a state.

[[LITERATURE REVIEW]]
Literature Review
The relationship between GMO use in cotton farming and the voting behavior for the Republican presidential candidate in West Virginia has inspired a range of scholarly investigations. Smith (2015) examined the agricultural landscape in West Virginia and its political implications, while Doe (2018) conducted a thorough analysis of GMO adoption in the state. Jones (2019) further explored voting patterns and party preferences in West Virginia. While these studies form the warp and weft of our understanding, there remains ample room for additional exploration and in-depth analysis.
Turning to the broader literature on agricultural biotechnology and its societal impact, "The Impact of Genetically Engineered Crops on Farm Sustainability in the United States" by Fernandez et al. (2014) sheds light on the broader implications of GMO adoption in farming. Similarly, "Biotechnology and the Agricultural Industry" by Brown (2017) offers a comprehensive examination of the economic and environmental dimensions of GMO use in agriculture. These works provide a strong foundation for contextualizing the potential influence of GMO cotton on political behavior in West Virginia.
In a departure from the more traditional academic literature, the fiction works "Cotton Gin and Tonic: A Political Thriller" by Author McAuthorface and "The Boll Weevil Conspiracy" by Mystery Writer X delve into the realm of political intrigue and agricultural drama. Though not rooted in objective data analysis, these narratives showcase the fertile ground for imaginative exploration of the potential entanglement between cotton farming and political machinations.
As part of the researchers' quest to gain a holistic understanding of West Virginia's political landscape, the TV shows "Coal and Cotton: Clash of Clans" and "Mountaineer Politics Unwound" were also explored for insights. While not providing empirical evidence, these shows offered a rich tapestry of anecdotal observations and dramatic depictions of the intersection between agriculture and politics in the state.
With this diverse array of literature at our disposal, we lay the groundwork for our own investigation into the GMO-cotton-Republican-vote nexus, aiming to tease out the strands that may connect these seemingly disparate domains.
Ah, the joys of scholarly inquiry mixed with a dollop of agricultural intrigue and political shenanigans! Let's embark on this comical and captivating journey through the cornfields and ballot boxes of West Virginia.

[[RESULTS]]
The foundation of our study lies in the analysis of the relationship between the usage of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. After an arduous excavation of data from the USDA, MIT Election Data and Science Lab, and the Harvard Dataverse, we unearthed a remarkable correlation coefficient of 0.9766999, an r-squared value of 0.9539427, and a p-value of less than 0.01 for the time span of 2000 to 2020. These figures indicate a robust and statistically significant relationship between the variables under scrutiny.
Fig. 1 illustrates the near-linear relationship between the two variables, akin to the razor-sharp precision of a cotton gin separating fibers from seeds. It's no cotton candy, folks - the data don't lie. The plot portrays a discernible rising trend, mirroring the steadfast support for the Republican party in West Virginia.
In this statistical landscape, the correlation coefficient of 0.9766999 stands as a towering redwood in the forest of data analysis. This coefficient signifies a nearly perfect positive relationship between the use of GMOs in cotton farming and the inclination to cast a Republican vote. It's a bit like finding a needle in a haystack - a correlation this strong is about as common as a four-leaf clover.
Furthermore, the r-squared value of 0.9539427 underscores that over 95% of the variability in the percentage of Republican votes can be explained by the usage of GMOs in cotton farming. If that doesn't make a compelling case for the influence of genetically modified cotton on politicking, I don't know what does.
The p-value of less than 0.01 adds a cherry on top of this genetically modified sundae. This minuscule p-value indicates that the observed association is highly unlikely to have occurred by mere chance. One might say it's as unlikely as finding a purple cow grazing in a field of white. In other words, the probability of this correlation being a fluke is about as slim as a GMO cotton seed.
In sum, our findings pave the way for further exploration of the intriguing links between agriculture and politics, with West Virginia serving as a ripe field for study. The robust statistical evidence we have presented may leave some scratching their heads, pondering whether we've stumbled upon a boll weevil invasion in the voting booth or simply unraveled the fabric of West Virginian political preference. Join us as we continue to plow through the fertile ground of GMOs and red votes, unraveling the intricate connections that shape the political quilt of the Mountain State.
So, there you have it - a statistical tale as intriguing as a mystery novel and as captivating as a gripping political drama. Let's continue to peel back the layers of this unlikely correlation and see where the threads of GMO cotton and Republican votes lead us next.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of the present study provide compelling evidence for the significant relationship between the usage of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. As we delved into the statistical landscape, the near-perfect positive correlation coefficient of 0.9766999 emerged as a towering redwood in the forest of data analysis, affirming the strong connection between these seemingly disparate variables. This finding aligns with prior research indicating a potential influence of agricultural practices on political inclinations.

Recalling the quirky elements of the literature review, we must take a moment to acknowledge the unexpected influence of fiction and television in shaping our understanding of the intersection between agriculture and politics. While not rooted in empirical analysis, the imaginative narratives and anecdotal observations from these sources offer a peculiar lens through which to view the potential entanglement of cotton farming and political machinations. It's as if the fictional world has woven its own thread into the fabric of our study, adding an unexpected and colorful dimension to our scholarly inquiry.

Turning back to the results, the r-squared value of 0.9539427 underscores the substantial extent to which the variability in the percentage of Republican votes can be explained by the usage of GMOs in cotton farming. This aligns with Fernandez et al.'s (2014) work on the broader implications of GMO adoption in farming, emphasizing the profound impact of agricultural biotechnology on societal phenomena. It's as if the agricultural landscape and political preference have been bundled together like a bale of cotton, with each tug on the thread revealing a deeper connection between these domains.

Moreover, the minuscule p-value of less than 0.01 adds an extra layer of statistical robustness to our findings, affirming the highly unlikely probability of the observed association occurring by mere chance. This echoes the sentiment conveyed in "The Boll Weevil Conspiracy" by Mystery Writer X, where the unexpected intertwining of agricultural drama and political intrigue takes center stage. It's as if the data itself is unfurling a gripping political drama, where each data point serves as a plot twist that propels the narrative forward.

In summary, our findings not only align with prior scholarly investigations but also bring to light the potential for a deeper and more nuanced understanding of the complex interplay between agricultural practices and political behavior. As we continue to unravel the intricate connections between GMO cotton and Republican votes, let us embrace the unexpected twists and turns that emerge, much like a captivating political thriller unfolding before our eyes. So, let's buckle up and prepare for the enthralling journey ahead - because in the field of scholarly inquiry, the unexpected can often lead to the most illuminating discoveries.

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 GMO use in cotton and Votes for the Republican Presidential candidate in West Virginia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Seeding Republican Votes: The GMO Connection in West Virginia Politics"

[[ABSTRACT]]
In this study, we delve into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. Armed with data from the USDA and MIT Election Data and Science Lab, as well as the Harvard Dataverse, we set out to uncover whether there's a statistical link between these seemingly unrelated entities. The findings reveal a striking correlation coefficient of 0.9766999 and a p-value of less than 0.01 during the period from 2000 to 2020, begging the question: is there a boll weevil in the voting booth, or have these data simply touched upon the fabric of West Virginian political preference? Join us as we unravel the intertwined threads of GMOs and red votes, and attempt to stitch together a coherent narrative in this agricultural and political quilt of a state.

[[INTRDUCTION]]
Introduction
The intersection of agriculture and politics has long been fertile ground for scholarly inquiry. In this paper, we embark on a whimsical journey through the rolling hills and hollers of West Virginia to explore the curious alignment between the use of genetically modified organisms (GMOs) in cotton farming and the voting patterns for the Republican presidential candidate. Like a cotton gin separating fiber from seed, we aim to discern whether there is a clear separation or an intertwining of these two seemingly distinct phenomena.
While West Virginia is renowned for its coal mining heritage, its agricultural sector, particularly in the cultivation of cotton, has quietly spun its own tale. At the same time, the state's political landscape, especially in presidential elections, has often defied national trends. If we may weave a metaphor, West Virginia's political fabric has been woven from red threads, with the state consistently showing robust support for the Republican party.
Straddling the worlds of agriculture and politics, we set out to unravel the Gordian knot between GMO cotton and Republican votes in the Mountain State. Much like genetically engineering a stronger strain of cotton, we have utilized a robust methodology and a trove of data to conduct a rigorous analysis.
The findings that we present in this paper are as surprising as encountering a kernel of popcorn in a bale of cotton. Our statistical analysis, spanning the years 2000 to 2020, has unveiled a correlation coefficient of 0.9766999 and a p-value of less than 0.01, suggesting a remarkably strong association between GMO cotton usage and the propensity to cast a Republican ballot in West Virginia. This correlation is so striking that one might be tempted to call it "corny," but let us resist the pun and proceed with solemnity.
As we delve into the heart of these intriguing findings, we invite you to join us on this expedition through the field of agricultural biotechnology and the labyrinthine corridors of West Virginia politics. Together, we shall sow the seeds of knowledge, cultivate a deeper understanding, and perhaps harvest some unexpected insights from this hybridization of seemingly disparate subjects. So, buckle up and brace yourselves for a journey that promises to be as bountiful as a cornucopia and as twisty as a strand of DNA!

[[RESULTS]]
The foundation of our study lies in the analysis of the relationship between the usage of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. After an arduous excavation of data from the USDA, MIT Election Data and Science Lab, and the Harvard Dataverse, we unearthed a remarkable correlation coefficient of 0.9766999, an r-squared value of 0.9539427, and a p-value of less than 0.01 for the time span of 2000 to 2020. These figures indicate a robust and statistically significant relationship between the variables under scrutiny.
Fig. 1 illustrates the near-linear relationship between the two variables, akin to the razor-sharp precision of a cotton gin separating fibers from seeds. It's no cotton candy, folks - the data don't lie. The plot portrays a discernible rising trend, mirroring the steadfast support for the Republican party in West Virginia.
In this statistical landscape, the correlation coefficient of 0.9766999 stands as a towering redwood in the forest of data analysis. This coefficient signifies a nearly perfect positive relationship between the use of GMOs in cotton farming and the inclination to cast a Republican vote. It's a bit like finding a needle in a haystack - a correlation this strong is about as common as a four-leaf clover.
Furthermore, the r-squared value of 0.9539427 underscores that over 95% of the variability in the percentage of Republican votes can be explained by the usage of GMOs in cotton farming. If that doesn't make a compelling case for the influence of genetically modified cotton on politicking, I don't know what does.
The p-value of less than 0.01 adds a cherry on top of this genetically modified sundae. This minuscule p-value indicates that the observed association is highly unlikely to have occurred by mere chance. One might say it's as unlikely as finding a purple cow grazing in a field of white. In other words, the probability of this correlation being a fluke is about as slim as a GMO cotton seed.
In sum, our findings pave the way for further exploration of the intriguing links between agriculture and politics, with West Virginia serving as a ripe field for study. The robust statistical evidence we have presented may leave some scratching their heads, pondering whether we've stumbled upon a boll weevil invasion in the voting booth or simply unraveled the fabric of West Virginian political preference. Join us as we continue to plow through the fertile ground of GMOs and red votes, unraveling the intricate connections that shape the political quilt of the Mountain State.
So, there you have it - a statistical tale as intriguing as a mystery novel and as captivating as a gripping political drama. Let's continue to peel back the layers of this unlikely correlation and see where the threads of GMO cotton and Republican votes lead us next.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

Through rigorous analysis, we have unveiled a compelling link between the usage of genetically modified organisms (GMOs) in cotton farming and the voting patterns for the Republican presidential candidate in West Virginia. Our findings, much like a perfectly spun cotton thread, have woven a seamless narrative of the intertwined relationship between agriculture and politics in the Mountain State.

The striking correlation coefficient of 0.9766999 and a p-value of less than 0.01 serve as the cornerstone of our study, making the association between GMO cotton usage and Republican votes as conspicuous as a bright red barn in an open field. This correlation stands as firm as the stalks of GMO cotton themselves, leaving little room for skepticism.

As to be expected, further research may dig deeper into the root causes of this connection, but for now, we can humorously declare that the bond between GMOs and red votes in West Virginia is as strong as the grip of a farmer on their favorite pitchfork. It seems West Virginians are as inclined to pick Republican candidates as they are to pick cotton from the fields.

In light of our compelling findings, we firmly assert that there is no need for additional research in this area. The results are as clear as a pristine acre of genetically modified cotton. With such a robust correlation, one might say this study has truly "seeded" itself as a cornerstone of agricultural and political research. It's time to reap what we've sown and move on to other pressing questions—like if there's a connection between avocado consumption and hipster votes in Brooklyn.

In conclusion, we have unveiled a statistical tapestry that ties the cultivation of GMO cotton to the voting habits of the people of West Virginia. The fabric of this connection is now plainly visible, leaving us to ponder what other surprising patterns may be lurking beneath the surface of seemingly unrelated domains. Let's not get tangled up in overanalyzing this result—sometimes, a correlation is as simple as a duck waddling through a cotton field.

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 Seeding Republican Votes: The GMO Connection in West Virginia Politics. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Botany

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 Seeding Republican Votes: The GMO Connection in West Virginia Politics. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Genetically Engineered 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 Seeding Republican Votes: The GMO Connection in West Virginia Politics with an abstract of In this study, we delve into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton farming and the percentage of votes for the Republican presidential candidate in West Virginia. Armed with data from the USDA and MIT Election Data and Science Lab, as well as the Harvard Dataverse, we set out to uncover whether there's a statistical link between these seemingly unrelated entities. The findings reveal a striking correlation coefficient of 0.9766999 and a p-value of less than 0.01 during the period from 2000 to 2020, begging the question: is there a boll weevil in the voting booth, or have these data simply touched upon the fabric of West Virginian political preference? Join us as we unravel the intertwined threads of GMOs and red votes, and attempt to stitch together a coherent narrative in this agricultural and political quilt of a state.

ChatGPT:

GMOs, genetically modified organisms, cotton farming, Republican votes, West Virginia politics, correlation coefficient, statistical link, USDA data, MIT Election Data and Science Lab, Harvard Dataverse, boll weevil, West Virginian political preference

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

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Data details

GMO use in cotton
Detailed data title: Percent of cotton planted in the United States genetically modified with both Bt and HT
Source: USDA
See what else correlates with GMO use in cotton

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

Correlation r = 0.9766999 (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 = 0.9539427 (Coefficient of determination)
This means 95.4% of the change in the one variable (i.e., Votes for the Republican Presidential candidate in West Virginia) is predictable based on the change in the other (i.e., GMO use in cotton) over the 6 years from 2000 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.000808.^Show 0.0008080161527901487000000000
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 0.98 in 0.0808% of random cases. Said differently, if you correlated 1,238 random variables^Note Which I absolutely did.
with the same 5 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 5 because we have two variables measured over a period of 6 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.

[ 0.8, 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.

	2000	2004	2008	2012	2016	2020
GMO use in cotton (GMO cotton %)	20	30	45	63	80	83
Votes for the Republican Presidential candidate in West Virginia (Percentage of votes)	51.9152	56.0707	55.7103	62.2958	68.6306	68.6316

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([20,30,45,63,80,83,])
array_2 = np.array([51.9152,56.0707,55.7103,62.2958,68.6306,68.6316,])
array_1_name = "GMO use in cotton"
array_2_name = "Votes for the Republican Presidential candidate in West Virginia"

# 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)

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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.

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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.
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Line chart for only GMO use in cotton
Line chart for only Votes for the Republican Presidential candidate in West Virginia
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The spurious research paper: Seeding Republican Votes: The GMO Connection in West Virginia Politics

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Correlation ID: 4452 · Black Variable ID: 801 · Red Variable ID: 26189


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