Spurious correlation #3,956 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Total U.S. grain export volume and the second variable is Biomass power generated in Qatar. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Total U.S. grain export volume and the second variable is Biomass power generated in Qatar. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the U.S. grain export volume rose, it led to a surplus of grains worldwide. This surplus grain somehow sparked a chain reaction, ultimately leading to a boost in Qatari biomass power generation. Perhaps the grains were so full of energy that just being in proximity to them was all it took to power up the biomass plants in Qatar. "Who would have thought that grains could be the secret fuel for Qatar's biomass power? It's like the grains were saying, 'We're not just for bread and cereal, we're also here to power up your day!'"

^{Show image generation prompt} Model: dalle-3
Prompt: [Patriotism] Imagine a colossal cargo ship laden with golden U.S. grains gliding toward the horizon. In the distance, a sprawling power plant in Qatar stands amidst the desert, its smokestacks reaching to the sky. The ship and the power plant are joined by an unseen thread, embodying the reciprocal bond between the grain-laden vessel and the burgeoning biomass power industry in Qatar. 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: Total U.S. grain export volume and Biomass power generated in Qatar. 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: Celtic art Symbolism Psychedelic Symbolism Vaporwave
ChatGPT: Symbolism

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 Total U.S. grain export volume and Biomass power generated in Qatar, in the following situation: Biomass power in Qatar increased because more U.S. grain exports meant more grain was available to be burned for energy, leading to an increase in biomass power generation in Qatar.. Keep the description simple.
ChatGPT: Sure! Picture a massive cargo ship filled with U.S. grains sailing towards Qatar. In the background, you see a power plant in Qatar with smoke rising from its chimneys, symbolizing the increased biomass power generation. The ship and the power plant are interconnected, depicting the direct relationship between the total U.S. grain export volume and the biomass power generated in Qatar.

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: [ Symbolism ] and depicting this scene: Sure! Picture a massive cargo ship filled with U.S. grains sailing towards Qatar. In the background, you see a power plant in Qatar with smoke rising from its chimneys, symbolizing the increased biomass power generation. The ship and the power plant are interconnected, depicting the direct relationship between the total U.S. grain export volume and the biomass power generated in Qatar. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [Patriotism] Imagine a colossal cargo ship laden with golden U.S. grains gliding toward the horizon. In the distance, a sprawling power plant in Qatar stands amidst the desert, its smokestacks reaching to the sky. The ship and the power plant are joined by an unseen thread, embodying the reciprocal bond between the grain-laden vessel and the burgeoning biomass power industry in Qatar.

*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 Total U.S. grain export volume caused Biomass power generated in Qatar to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar

Journal of Agro-Energy Economics and Policy

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 Total U.S. grain export volume and Biomass power generated in Qatar. 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar"

[[ABSTRACT]]
In this study, we delve into the intercontinental relationship between Total U.S. grain export volume and Biomass power generated in the State of Qatar. You might say we're tackling a topic that's truly... grainy. Utilizing data from Statista and the Energy Information Administration, we quantified and examined the statistical association between these seemingly unrelated entities. Our findings unveiled a remarkably high correlation coefficient of 0.8666606 and a p-value of less than 0.01 for the period spanning from 2012 to 2021. That's a-maize-ing! This suggests a strong positive linear relationship between the two variables, indicating that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit. We leave no kernel unturned in exploring the potential mechanisms underlying this grain-to-power dynamic. Our results not only shed light on an intriguing link between agriculture and energy production but also offer food for thought in understanding the global flow of grains and the cultivation of renewable energy sources.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The world of scientific research is often seen as a serious and solemn pursuit, with researchers toiling away in laboratories and poring over data in pursuit of groundbreaking discoveries. But who says we can't inject a little levity into the mix? In this study, we set out to uncover the corn-nection between Total U.S. grain export volume and Biomass power generated in the State of Qatar. Don't worry, we promise to keep the puns to a *barley* acceptable level.
As the saying goes, "Where there's a grain, there's a whey!" The intersection of agricultural exports and renewable energy production may not be the first thing that comes to mind when discussing global dynamics, but our research aims to show that the relationship between these two seemingly disparate elements is a lot more than just a kernel of truth. Oh, the *ear*ony!
Drawing on data from Statista and the Energy Information Administration, we traded our lab coats for statistical analysis and unearthed a correlation coefficient of 0.8666606 and a p-value of less than 0.01 for the period from 2012 to 2021. Talk about a *corn-y* statistic! This statistical association indicates a strong positive linear relationship between U.S. grain exports and Biomass power generation in Qatar. It's as clear as day: when it comes to this relationship, we certainly didn't *kernel* the details.
Now, you might wonder, what's the grain deal with this connection? Our findings suggest that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit. It's as if these two variables are *stalk*-ing each other! But fear not, we won't just *wheat* our appetite with correlations – we aim to dig deep and bring to the surface the complex mechanisms that fuel this grain-to-power dynamic. After all, in the world of science, there's always *millet* to learn!
So, buckle up and prepare to embark on a journey through the fields of agriculture and the power plants of renewable energy. Our results not only provide insight into the intriguing link between grain trade and energy production but also serve up some food for thought in understanding the interconnected web of global food and energy systems. Oh, the things you can *barley* believe when you dive into the world of research!

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In “The Intercontinental Dynamics of Agricultural Trade” by Smith et al., the authors find lorem and ipsum, elucidating the intricate relationship between U.S. grain exports and global agricultural markets. Similarly, Jones’ study “Energy Harvest: Unraveling the Mysteries of Biomass Power Generation” delves into the mechanisms of renewable energy production, shedding light on the factors influencing Biomass power generation in various regions.

However, when it comes to uncovering the corn-nection between Total U.S. grain export volume and Biomass power generated in Qatar, it's essential to explore a range of literature, not just the serious stuff. Therefore, we must not overlook the insights offered in non-fiction books such as "The Omnivore's Dilemma" by Michael Pollan and "Grain Brain" by David Perlmutter. These works provide valuable context for understanding the impact of grain production on human diets and health, an important consideration in the broader discussion of grain utilization.

In addition to non-fiction works, it's worth considering the potential influence of fiction literature on our understanding of grain-related dynamics. For instance, the popular science fiction novel "Dune" by Frank Herbert, with its portrayal of a desert planet reliant on spice trade, may offer a metaphorical glimpse into the complexities of agricultural trade and resource dependence. And let’s not forget the board game "Agricola," where players vie for resources and cultivate their farms – a humorous take on the very real-world challenges of agricultural production and distribution.

Now, back to the serious stuff - it's clear that the corn-nection between U.S. grain exports and Biomass power generation in Qatar is not just a *kernel* of truth, but a significant aspect of global agricultural and energy dynamics. As we navigate through the maze of existing literature, it’s crucial to approach this topic with a mix of scholarly rigor and a healthy dose of good humor. After all, when it comes to researching the connection between grains and power, there's no harm in adding a bit of spice to the academic stew... just not too much paprika, we don't want to get corn-fused!

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 Total U.S. grain export volume and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Statista and Energy Information Administration . You used data from 2012 to 2021

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar"

[[ABSTRACT]]
In this study, we delve into the intercontinental relationship between Total U.S. grain export volume and Biomass power generated in the State of Qatar. You might say we're tackling a topic that's truly... grainy. Utilizing data from Statista and the Energy Information Administration, we quantified and examined the statistical association between these seemingly unrelated entities. Our findings unveiled a remarkably high correlation coefficient of 0.8666606 and a p-value of less than 0.01 for the period spanning from 2012 to 2021. That's a-maize-ing! This suggests a strong positive linear relationship between the two variables, indicating that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit. We leave no kernel unturned in exploring the potential mechanisms underlying this grain-to-power dynamic. Our results not only shed light on an intriguing link between agriculture and energy production but also offer food for thought in understanding the global flow of grains and the cultivation of renewable energy sources.

[[INTRODUCTION]]
The world of scientific research is often seen as a serious and solemn pursuit, with researchers toiling away in laboratories and poring over data in pursuit of groundbreaking discoveries. But who says we can't inject a little levity into the mix? In this study, we set out to uncover the corn-nection between Total U.S. grain export volume and Biomass power generated in the State of Qatar. Don't worry, we promise to keep the puns to a *barley* acceptable level.
As the saying goes, "Where there's a grain, there's a whey!" The intersection of agricultural exports and renewable energy production may not be the first thing that comes to mind when discussing global dynamics, but our research aims to show that the relationship between these two seemingly disparate elements is a lot more than just a kernel of truth. Oh, the *ear*ony!
Drawing on data from Statista and the Energy Information Administration, we traded our lab coats for statistical analysis and unearthed a correlation coefficient of 0.8666606 and a p-value of less than 0.01 for the period from 2012 to 2021. Talk about a *corn-y* statistic! This statistical association indicates a strong positive linear relationship between U.S. grain exports and Biomass power generation in Qatar. It's as clear as day: when it comes to this relationship, we certainly didn't *kernel* the details.
Now, you might wonder, what's the grain deal with this connection? Our findings suggest that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit. It's as if these two variables are *stalk*-ing each other! But fear not, we won't just *wheat* our appetite with correlations – we aim to dig deep and bring to the surface the complex mechanisms that fuel this grain-to-power dynamic. After all, in the world of science, there's always *millet* to learn!
So, buckle up and prepare to embark on a journey through the fields of agriculture and the power plants of renewable energy. Our results not only provide insight into the intriguing link between grain trade and energy production but also serve up some food for thought in understanding the interconnected web of global food and energy systems. Oh, the things you can *barley* believe when you dive into the world of research!

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

When it comes to unraveling the maize of connections between U.S. grain exports and Biomass power generation in Qatar, our research methodology took a multi-pronged approach. We gathered data from a variety of sources, mainly relying on esteemed repositories such as Statista and the Energy Information Administration. These sources provided us with a *grain*-d amount of information to chew on, allowing us to sink our teeth into the statistical analysis at hand.

To establish the relationship between U.S. grain exports and Biomass power generation in Qatar, we employed a time-series analysis approach that could cut through the *stalk* of complexity and capture the dynamics between these two variables. Our statistical exploration involved some advanced methods that were nothing short of *corn*-plexity itself. We utilized Autoregressive Integrated Moving Average (ARIMA) models to capture the time-dependent nature of the data, because when it comes to analyzing these intercontinental connections, we can't just *ear* it easy!

Furthermore, we conducted a Granger causality test to determine the direction of influence between U.S. grain exports and Biomass power generation in Qatar. We weren't just content with throwing numbers around; we wanted to *harvest* concrete evidence of causation, *rye* and straightforward. After all, in the world of research, we can't simply rely on *flour*y language – we need to back our findings with robust statistical methods.

In addition to these sophisticated statistical analyses, we also delved into a qualitative examination of the broader economic and environmental factors that could *sprout* the observed relationship between grain exports and Biomass power generation. It wasn't just about crunching numbers; we wanted to paint a *corn*-prehensive picture of the forces at play, digging *barley* beneath the surface to understand the *grain* reality.

But wait, don't pop-corn for a second – there's more! We weren't content with just looking at the direct relationship between these variables; we also explored potential *kernel*-lations with other relevant factors such as global economic trends, climate patterns, and international energy policies. We wanted to ensure that our findings weren't just a *corn*-cidence, but rather a part of a larger, interconnected *cob*-web of influences.

In the grand tradition of scientific inquiry, our research methodology was designed to leave no stone unturned, no cornstalk left *unshucked*. We combined the rigor of statistical analysis with the insight of qualitative examination, creating a methodological fusion that's as robust as it is *corny*. With our approach, we aimed to not only uncover the statistical relationship between U.S. grain exports and Biomass power generation in Qatar but to also cultivate a deeper understanding of the underlying forces at play.

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 Total U.S. grain export volume and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar"

[[ABSTRACT]]
In this study, we delve into the intercontinental relationship between Total U.S. grain export volume and Biomass power generated in the State of Qatar. You might say we're tackling a topic that's truly... grainy. Utilizing data from Statista and the Energy Information Administration, we quantified and examined the statistical association between these seemingly unrelated entities. Our findings unveiled a remarkably high correlation coefficient of 0.8666606 and a p-value of less than 0.01 for the period spanning from 2012 to 2021. That's a-maize-ing! This suggests a strong positive linear relationship between the two variables, indicating that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit. We leave no kernel unturned in exploring the potential mechanisms underlying this grain-to-power dynamic. Our results not only shed light on an intriguing link between agriculture and energy production but also offer food for thought in understanding the global flow of grains and the cultivation of renewable energy sources.

[[LITERATURE REVIEW]]
In “The Intercontinental Dynamics of Agricultural Trade” by Smith et al., the authors find lorem and ipsum, elucidating the intricate relationship between U.S. grain exports and global agricultural markets. Similarly, Jones’ study “Energy Harvest: Unraveling the Mysteries of Biomass Power Generation” delves into the mechanisms of renewable energy production, shedding light on the factors influencing Biomass power generation in various regions.
However, when it comes to uncovering the corn-nection between Total U.S. grain export volume and Biomass power generated in Qatar, it's essential to explore a range of literature, not just the serious stuff. Therefore, we must not overlook the insights offered in non-fiction books such as "The Omnivore's Dilemma" by Michael Pollan and "Grain Brain" by David Perlmutter. These works provide valuable context for understanding the impact of grain production on human diets and health, an important consideration in the broader discussion of grain utilization.
In addition to non-fiction works, it's worth considering the potential influence of fiction literature on our understanding of grain-related dynamics. For instance, the popular science fiction novel "Dune" by Frank Herbert, with its portrayal of a desert planet reliant on spice trade, may offer a metaphorical glimpse into the complexities of agricultural trade and resource dependence. And let’s not forget the board game "Agricola," where players vie for resources and cultivate their farms – a humorous take on the very real-world challenges of agricultural production and distribution.
Now, back to the serious stuff - it's clear that the corn-nection between U.S. grain exports and Biomass power generation in Qatar is not just a *kernel* of truth, but a significant aspect of global agricultural and energy dynamics. As we navigate through the maze of existing literature, it’s crucial to approach this topic with a mix of scholarly rigor and a healthy dose of good humor. After all, when it comes to researching the connection between grains and power, there's no harm in adding a bit of spice to the academic stew... just not too much paprika, we don't want to get corn-fused!

[[RESULTS]]
In analyzing the relationship between Total U.S. grain export volume and Biomass power generated in Qatar, we found a correlation coefficient of 0.8666606, indicating a strong positive linear relationship between these seemingly distinct variables. It seems that when it comes to grains and power, there's more than just *kernel* of truth to the connection. Perhaps we can call it the "bread and watts" of international trade!
The r-squared value of 0.7511007 further validates the robustness of this relationship. It's like the bond between bread and butter – they just go together so well!
The p-value of less than 0.01 suggests that the likelihood of observing such a strong relationship by chance is exceedingly low. This connection between grain exports and Biomass power generation in Qatar is certainly not just a statistical *stalk*!
Our results are beautifully encapsulated in Figure 1, which shows a scatterplot illustrating the strong positive correlation between Total U.S. grain export volume and Biomass power generated in Qatar. It's as clear as day, much like a perfectly ripe ear of corn in a field of green.
The grains have indeed spoken, revealing a compelling connection with the power generated in Qatar. It's as if the grains are whispering, "Let's get this bread, and power the world while we're at it!"

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study provide compelling support for the previously explored literature on the intercontinental dynamics of agricultural trade and biomass energy generation, and they also offer some food for thought in the realm of pun-tastic academic research. Our results revealed a strong positive linear relationship between Total U.S. grain export volume and Biomass power generated in Qatar, echoing the sentiments of Smith et al. and Jones who laid the groundwork for uncovering these interconnected trends. It's abundantly clear that this *grain-to-power* dynamic is not just a *corny* hypothesis, but a tangible reality in the world of international trade and energy production.

The high correlation coefficient of 0.8666606 speaks volumes about the tight bond between these seemingly distinct variables, demonstrating that the connection between grains and power is more than just a *kernel* of truth. It's a relationship as enduring as that of peanut butter and jelly – or, in this case, wheat and watts! Our results substantiate the notion that when U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit, underscoring the significance of global agricultural markets in shaping energy production dynamics. You might say it's a prime example of *harvesting* the power potential of grains.

The r-squared value of 0.7511007 further solidifies the robustness of this association, akin to the stability of a well-crafted scientific theory. It's like the reliable predictability of a well-baked loaf of bread – you can count on this relationship to hold true. Meanwhile, the p-value of less than 0.01 underscores the statistical significance of our findings, convincingly debunking any notion that this connection is merely a statistical *stalk*. This substantial p-value indicates that the likelihood of observing such a strong relationship by chance is about as rare as finding a single orange Skittle in a sea of vibrant yellows – it's just not a common occurrence!

The scatterplot in Figure 1 vividly portrays the strength of the relationship, leaving little room for doubt. It's as crystal clear as a scientific observation made on a sunny day, or as clear as the difference between "ear corn" and "yearn for corn." Our findings contribute to a deeper understanding of the role of agricultural trade in shaping energy production, offering valuable insights into the global flow of grains and the cultivation of renewable energy sources. It's almost as if the grains themselves are seeking to power the world, whispering, "We *knead* to make a difference – and we're not just *stalk*-ing you!"

In conclusion, our results not only affirm the findings of prior research but also bring levity to the serious study of grain export and energy generation dynamics. It's a reminder that even in the world of scholarly inquiry, there's always room for a good pun or two. After all, when it comes to research, a little bit of humor might just be the yeast that raises the quality of our discussions.

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 Total U.S. grain export volume and Biomass power generated in Qatar. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar"

[[ABSTRACT]]
In this study, we delve into the intercontinental relationship between Total U.S. grain export volume and Biomass power generated in the State of Qatar. You might say we're tackling a topic that's truly... grainy. Utilizing data from Statista and the Energy Information Administration, we quantified and examined the statistical association between these seemingly unrelated entities. Our findings unveiled a remarkably high correlation coefficient of 0.8666606 and a p-value of less than 0.01 for the period spanning from 2012 to 2021. That's a-maize-ing! This suggests a strong positive linear relationship between the two variables, indicating that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit. We leave no kernel unturned in exploring the potential mechanisms underlying this grain-to-power dynamic. Our results not only shed light on an intriguing link between agriculture and energy production but also offer food for thought in understanding the global flow of grains and the cultivation of renewable energy sources.

[[INTRDUCTION]]
The world of scientific research is often seen as a serious and solemn pursuit, with researchers toiling away in laboratories and poring over data in pursuit of groundbreaking discoveries. But who says we can't inject a little levity into the mix? In this study, we set out to uncover the corn-nection between Total U.S. grain export volume and Biomass power generated in the State of Qatar. Don't worry, we promise to keep the puns to a *barley* acceptable level.
As the saying goes, "Where there's a grain, there's a whey!" The intersection of agricultural exports and renewable energy production may not be the first thing that comes to mind when discussing global dynamics, but our research aims to show that the relationship between these two seemingly disparate elements is a lot more than just a kernel of truth. Oh, the *ear*ony!
Drawing on data from Statista and the Energy Information Administration, we traded our lab coats for statistical analysis and unearthed a correlation coefficient of 0.8666606 and a p-value of less than 0.01 for the period from 2012 to 2021. Talk about a *corn-y* statistic! This statistical association indicates a strong positive linear relationship between U.S. grain exports and Biomass power generation in Qatar. It's as clear as day: when it comes to this relationship, we certainly didn't *kernel* the details.
Now, you might wonder, what's the grain deal with this connection? Our findings suggest that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit. It's as if these two variables are *stalk*-ing each other! But fear not, we won't just *wheat* our appetite with correlations – we aim to dig deep and bring to the surface the complex mechanisms that fuel this grain-to-power dynamic. After all, in the world of science, there's always *millet* to learn!
So, buckle up and prepare to embark on a journey through the fields of agriculture and the power plants of renewable energy. Our results not only provide insight into the intriguing link between grain trade and energy production but also serve up some food for thought in understanding the interconnected web of global food and energy systems. Oh, the things you can *barley* believe when you dive into the world of research!

[[RESULTS]]
In analyzing the relationship between Total U.S. grain export volume and Biomass power generated in Qatar, we found a correlation coefficient of 0.8666606, indicating a strong positive linear relationship between these seemingly distinct variables. It seems that when it comes to grains and power, there's more than just *kernel* of truth to the connection. Perhaps we can call it the "bread and watts" of international trade!
The r-squared value of 0.7511007 further validates the robustness of this relationship. It's like the bond between bread and butter – they just go together so well!
The p-value of less than 0.01 suggests that the likelihood of observing such a strong relationship by chance is exceedingly low. This connection between grain exports and Biomass power generation in Qatar is certainly not just a statistical *stalk*!
Our results are beautifully encapsulated in Figure 1, which shows a scatterplot illustrating the strong positive correlation between Total U.S. grain export volume and Biomass power generated in Qatar. It's as clear as day, much like a perfectly ripe ear of corn in a field of green.
The grains have indeed spoken, revealing a compelling connection with the power generated in Qatar. It's as if the grains are whispering, "Let's get this bread, and power the world while we're at it!"

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has illuminated the surprisingly strong positive linear relationship between Total U.S. grain export volume and Biomass power generated in Qatar, lending a whole new meaning to the phrase "grains of power." It's clear that these two variables are as interconnected as peas in a pod or, should I say, grains in a bin.

With a correlation coefficient of 0.8666606 and an r-squared value of 0.7511007, this relationship can't be dismissed as just another kernel of statistical noise. It's time to recognize that the grain-to-power dynamic is more than just a-maize-ing – it's downright electrifying!

Our findings suggest that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit, proving that when it comes to international trade and renewable energy, there's more than just bread on the table – there's also a whole lot of watts! It's like they're doing the conduction conga while we stand back and awe at the power of correlation.

As for the p-value of less than 0.01, it's safe to say that the likelihood of this relationship occurring by chance is about as rare as finding a four-leaf clover in a field of wheat.

In the grand scheme of things, our research not only provides insight into the fascinating linkage between grain trade and energy production but also serves up some food for thought in understanding the global flow of grains and the cultivation of renewable energy sources. It's a reminder that in the world of scientific exploration, there's always a field ripe for discovery.

Ultimately, this study calls for a toast – or should I say a roast, since we're dealing with grains – to the fruitful connection between U.S. grain exports and the generation of Biomass power in Qatar. Our comprehensive analysis has truly put the "corn" in cornerstone and the "wheat" in worthwhile.

In conclusion, it's safe to say that no further research is needed in this area – we've cracked the code on this grainy relationship, leaving no soy bean-ath the surface. It's time to butter our bread elsewhere!

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 Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Agro-Energy Economics and Policy

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 Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar. Respond only with the name of a fictional organization.

ChatGPT:

The Association for Bioenergy and Agricultural Research (ABAR)

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 Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar with an abstract of In this study, we delve into the intercontinental relationship between Total U.S. grain export volume and Biomass power generated in the State of Qatar. You might say we're tackling a topic that's truly... grainy. Utilizing data from Statista and the Energy Information Administration, we quantified and examined the statistical association between these seemingly unrelated entities. Our findings unveiled a remarkably high correlation coefficient of 0.8666606 and a p-value of less than 0.01 for the period spanning from 2012 to 2021. That's a-maize-ing! This suggests a strong positive linear relationship between the two variables, indicating that as U.S. grain exports fluctuate, Biomass power generation in Qatar follows suit. We leave no kernel unturned in exploring the potential mechanisms underlying this grain-to-power dynamic. Our results not only shed light on an intriguing link between agriculture and energy production but also offer food for thought in understanding the global flow of grains and the cultivation of renewable energy sources.

ChatGPT:

U.S. grain exports, biomass energy generation, Qatar, intercontinental relationship, statistical association, correlation coefficient, p-value, agriculture, energy production, renewable energy sources

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

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Data details

Total U.S. grain export volume
Source: Statista
See what else correlates with Total U.S. grain export volume

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

Correlation r = 0.8666606 (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 = 0.7511007 (Coefficient of determination)
This means 75.1% of the change in the one variable (i.e., Biomass power generated in Qatar) is predictable based on the change in the other (i.e., Total U.S. grain export volume) over the 10 years from 2012 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0012.^Show 0.0011737415649074254000000000
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 0.87 in 0.12% of random cases. Said differently, if you correlated 852 random variables^Note Which I absolutely did.
with the same 9 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 9 because we have two variables measured over a period of 10 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.

[ 0.52, 0.97 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Total U.S. grain export volume (Million Metric Tons)	51.5	74.9	80.5	78.7	93.7	92.5	81.2	77.4	104.4	96.4
Biomass power generated in Qatar (Billion kWh)	0.01	0.076	0.102	0.119	0.123	0.129	0.135	0.118	0.134	0.135068

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([51.5,74.9,80.5,78.7,93.7,92.5,81.2,77.4,104.4,96.4,])
array_2 = np.array([0.01,0.076,0.102,0.119,0.123,0.129,0.135,0.118,0.134,0.135068,])
array_1_name = "Total U.S. grain export volume"
array_2_name = "Biomass power generated in Qatar"

# 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 Total U.S. grain export volume
Line chart for only Biomass power generated in Qatar
AI-generated correlation image
The spurious research paper: Grains of Power: Uncovering the Corn-nection between U.S. Grain Exports and Biomass Energy Generation in Qatar

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Correlation ID: 3956 · Black Variable ID: 293 · Red Variable ID: 23912


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