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Spurious correlation #1,287 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of judges in New Mexico and the second variable is Solar power generated in Honduras.  The chart goes from 2012 to 2021, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As the number of judges in New Mexico increased, so did the frequency of their 'solar power' gavel bangs, inadvertently sending positive energy waves to Honduras, thus boosting solar power generation there. It seems the judiciary system has truly become a powerhouse for renewable energy - delivering justice and sunny dispositions in more ways than one!

Model: dalle-3
Prompt: [ Concept art ] Generate an image of a bustling courtroom in New Mexico, with a focus on the judges taking their seats behind their desks, each wielding a gavel. The scene should convey a sense of increasing power as more and more judges join the assembly, capturing the rising intensity of the gavels striking the desks. Emphasize the vibrations traveling through the floor and resonating through the Earth's crust, creating seismic waves that ripple outward. Contrast this with an image of the bright sun shining over Honduras, capturing the solar rays becoming vibrant and energized by the seismic waves, leading to a surge in solar power generation. The visual should artistically illustrate the interconnectedness of the events, depicting the moments of increasing intensity and energy. 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 situation described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here is a situation: Because as the number of judges in New Mexico increases, so does the gavel power, which resonates through the Earth's crust, creating seismic waves that energize the sun in Honduras, thereby increasing solar power generation. It's a judicially charged connection that's bringing a whole new meaning to renewable energy!. Given that situation, which of the following image descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand and the tone of the explanation. Please respond with only one of the following potential descriptions: Photorealism Glitch art An image that looks like it is a scene from 'Star Wars' Surrealism Concept art
ChatGPT: Concept art

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated and the explanation described. Do not reference graphs or charts; describe physical objects.
User prompt: Please describe a visual scene that depicts the following situation. Keep the description simple. Because as the number of judges in New Mexico increases, so does the gavel power, which resonates through the Earth's crust, creating seismic waves that energize the sun in Honduras, thereby increasing solar power generation. It's a judicially charged connection that's bringing a whole new meaning to renewable energy!
ChatGPT: In a busy courtroom in New Mexico, more and more judges take their seats behind their desks, each armed with a gavel. With each new judge, the sound of gavels striking the desks grows louder and more powerful. The vibrations travel through the floor and resonate through the Earth's crust, creating tiny seismic waves that ripple outward. Far away in Honduras, the sun shines brightly, its rays becoming stronger and more energized by the seismic waves, boosting solar power generation to new heights.

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: [ Concept art ] and depicting this scene: In a busy courtroom in New Mexico, more and more judges take their seats behind their desks, each armed with a gavel. With each new judge, the sound of gavels striking the desks grows louder and more powerful. The vibrations travel through the floor and resonate through the Earth's crust, creating tiny seismic waves that ripple outward. Far away in Honduras, the sun shines brightly, its rays becoming stronger and more energized by the seismic waves, boosting solar power generation to new heights. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Concept art ] Generate an image of a bustling courtroom in New Mexico, with a focus on the judges taking their seats behind their desks, each wielding a gavel. The scene should convey a sense of increasing power as more and more judges join the assembly, capturing the rising intensity of the gavels striking the desks. Emphasize the vibrations traveling through the floor and resonating through the Earth's crust, creating seismic waves that ripple outward. Contrast this with an image of the bright sun shining over Honduras, capturing the solar rays becoming vibrant and energized by the seismic waves, leading to a surge in solar power generation. The visual should artistically illustrate the interconnectedness of the events, depicting the moments of increasing intensity and energy.

*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.

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 The number of judges in New Mexico caused Solar power generated in Honduras to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)
Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras

Journal of Solar Legal Studies

Jan 2024

Reminder: This paper is AI-generated. Not real!
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 witty academic researcher. Your writing sounds like it belongs in a statistics journal.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between The number of judges in New Mexico and Solar power generated in Honduras. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Make the title a clever pun.

Your research team used data from Bureau of Larbor Statistics and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.9509355 and p < 0.01 for 2012 to 2021.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
"Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras"

[[ABSTRACT]]
In this research, we delve into the uncharted territory of connecting the seemingly unrelated realms of judicial activity in New Mexico and solar power generation in Honduras. Using data from the Bureau of Labor Statistics and the Energy Information Administration, we embark on a statistical journey to understand the curious correlation between these two disparate factors. Our findings reveal a striking correlation coefficient of 0.9509355 with p < 0.01 for the period spanning 2012 to 2021, shedding light on the perplexing relationship between the number of judges and solar power output. The implications of these unexpected findings leave us both judicially and energetically intrigued, inviting further exploration and prompting a reevaluation of the intersections between law and the radiant power of the sun.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft the introduction section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the introduction section of an academic research paper presenting the findings of the connection between The number of judges in New Mexico and Solar power generated in Honduras. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Here is the title and abstract of the paper:
[[TITLE]]
"Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras"

[[ABSTRACT]]
In this research, we delve into the uncharted territory of connecting the seemingly unrelated realms of judicial activity in New Mexico and solar power generation in Honduras. Using data from the Bureau of Labor Statistics and the Energy Information Administration, we embark on a statistical journey to understand the curious correlation between these two disparate factors. Our findings reveal a striking correlation coefficient of 0.9509355 with p < 0.01 for the period spanning 2012 to 2021, shedding light on the perplexing relationship between the number of judges and solar power output. The implications of these unexpected findings leave us both judicially and energetically intrigued, inviting further exploration and prompting a reevaluation of the intersections between law and the radiant power of the sun.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

The connection between judicial activity and solar power generation may initially seem as mismatched as wearing a robe in a courtroom. Yet, it is precisely through probing such curious correlations that we can uncover new insights and shed light on hidden relationships. In this study, we aim to investigate the unexpected link between these two seemingly unrelated variables—namely, the number of judges in New Mexico and the solar energy production in Honduras. While the link may appear about as clear as a cloudy day, our statistical analysis promises to illuminate the connection, much like the sun peeking through the clouds.

The notion of judicial activity impacting solar energy production may seem far-fetched, but as we embark on this rigorous exploration, it becomes apparent that there are more underlying dynamics at play than meet the eye. Beyond their apparent dissimilarity, both factors are deeply entrenched in the intricate frameworks of institutional and environmental influences, making them ripe for investigation. By leveraging datasets from the Bureau of Labor Statistics and the Energy Information Administration, we aim to cast a spotlight on this unusual relationship, and perhaps surprise ourselves and others with the brilliance of our findings. As we dig deeper into the data, we are reminded of the age-old advice to always approach research with an open mind, for you never know when you might stumble upon a solar-powered breakthrough.

Though the trail we are blazing may seem unorthodox, it is precisely in these uncharted territories that new discoveries and insights emerge. Our approach is not unlike the process of harnessing sunlight—a seemingly mundane and constant occurrence, and yet, when channeled and examined with precision, it can reveal dimensions of our world previously unseen. With this investigation, we hope to ignite an intellectual spark that not only piques the interest of researchers and policymakers, but also brings a radiant smile to the faces of those seeking knowledge in unexpected places.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 The number of judges in New Mexico and Solar power generated in Honduras. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
"Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras"

[[ABSTRACT]]
In this research, we delve into the uncharted territory of connecting the seemingly unrelated realms of judicial activity in New Mexico and solar power generation in Honduras. Using data from the Bureau of Labor Statistics and the Energy Information Administration, we embark on a statistical journey to understand the curious correlation between these two disparate factors. Our findings reveal a striking correlation coefficient of 0.9509355 with p < 0.01 for the period spanning 2012 to 2021, shedding light on the perplexing relationship between the number of judges and solar power output. The implications of these unexpected findings leave us both judicially and energetically intrigued, inviting further exploration and prompting a reevaluation of the intersections between law and the radiant power of the sun.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The connection between judicial activity and solar power generation may initially seem as mismatched as wearing a robe in a courtroom. Yet, it is precisely through probing such curious correlations that we can uncover new insights and shed light on hidden relationships. In this study, we aim to investigate the unexpected link between these two seemingly unrelated variables—namely, the number of judges in New Mexico and the solar energy production in Honduras. While the link may appear about as clear as a cloudy day, our statistical analysis promises to illuminate the connection, much like the sun peeking through the clouds.
The notion of judicial activity impacting solar energy production may seem far-fetched, but as we embark on this rigorous exploration, it becomes apparent that there are more underlying dynamics at play than meet the eye. Beyond their apparent dissimilarity, both factors are deeply entrenched in the intricate frameworks of institutional and environmental influences, making them ripe for investigation. By leveraging datasets from the Bureau of Labor Statistics and the Energy Information Administration, we aim to cast a spotlight on this unusual relationship, and perhaps surprise ourselves and others with the brilliance of our findings. As we dig deeper into the data, we are reminded of the age-old advice to always approach research with an open mind, for you never know when you might stumble upon a solar-powered breakthrough.
Though the trail we are blazing may seem unorthodox, it is precisely in these uncharted territories that new discoveries and insights emerge. Our approach is not unlike the process of harnessing sunlight—a seemingly mundane and constant occurrence, and yet, when channeled and examined with precision, it can reveal dimensions of our world previously unseen. With this investigation, we hope to ignite an intellectual spark that not only piques the interest of researchers and policymakers, but also brings a radiant smile to the faces of those seeking knowledge in unexpected places.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The findings presented in this literature review reflect an eclectic array of studies that have probed the connection between seemingly incongruous variables, drawing a tenuous thread between the number of judges in New Mexico and solar energy production in Honduras. Smith et al. (2015) first broached the topic in their seminal work, "Judicial Patterns and Solar Surprises: A Statistical Odyssey." This groundbreaking study laid the foundation for exploring the potential interplay between judicial activity and solar power output, paving the way for subsequent investigations.

Following suit, Doe (2017) contributed to this burgeoning field with "Illuminating Justice: A Comparative Analysis of Judicial Luminosity and Solar Energy Potential" expanding the discourse to encompass not only the sheer quantity of judges but also the radiance they emit, in a figurative sense. Jones (2019) further advanced this line of inquiry in "Sunshine in the Courtroom: Exploring the Influence of Solar Power on Judicial Decision-Making" by investigating the potential reciprocal influence of solar energy on judicial reasoning. These scholarly endeavors provided a solid foundation for us to construct our own empirical analysis, despite the rather sunny disposition of this research topic.

Venturing beyond the academic landscape, a foray into related non-fiction literature reveals an eclectic mix of texts that tangentially touch upon elements of this peculiar correlation. "Solar Power and Legal Empowerment" by Greenleaf (2018) takes a sociological approach to the intersection of solar energy and legal systems, offering a multidisciplinary perspective that illuminates hitherto unexplored aspects of our topic. Additionally, "Judicial Beaming: A Radiant History" by Brightman (2016) dissects the historical context of judicial luminosity, shedding light on the overlooked influence of sunlight on legal proceedings.

On a more imaginative note, the fictional realm also offers curious parallels to our investigation. "The Sun Also Judges" by Hemmingway (1926) exemplifies the potential for literary allusions to our research, painting a picturesque narrative where the sun takes on the role of a discerning judge, casting its warm rays of judgment upon the characters. Similarly, "Solar Scales of Justice" by Christie (1930) weaves a tale of legal intrigue against a backdrop of solar symbolism, hinting at the deeper connections that may underpin our seemingly disparate variables.

In a cinematic context, while there may not be an explicitly titled film on our precise topic of interest, "A Few Good Panels" (1992) and "Solar Justice" (2008) are examples of movies that, when viewed through the lens of creative interpretation, can provide some tangential insights into the overlapping themes of justice and solar energy. These cinematic forays, while not directly related to the scholarly literature landscape, offer a lighthearted parallel to the serious investigation at hand, reminding us that unexpected connections may lurk in the most unlikely places.

As we assimilate this diverse array of literature, it becomes evident that the connections we seek may not always be glaringly apparent, much like the elusive nature of a solar eclipse. Nevertheless, we press onward with enthusiasm, poised to unravel the sunny mysteries that lie at the intersection of judicial activity and solar power generation.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 The number of judges in New Mexico and Solar power generated in Honduras. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Bureau of Larbor Statistics 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]]
"Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras"

[[ABSTRACT]]
In this research, we delve into the uncharted territory of connecting the seemingly unrelated realms of judicial activity in New Mexico and solar power generation in Honduras. Using data from the Bureau of Labor Statistics and the Energy Information Administration, we embark on a statistical journey to understand the curious correlation between these two disparate factors. Our findings reveal a striking correlation coefficient of 0.9509355 with p < 0.01 for the period spanning 2012 to 2021, shedding light on the perplexing relationship between the number of judges and solar power output. The implications of these unexpected findings leave us both judicially and energetically intrigued, inviting further exploration and prompting a reevaluation of the intersections between law and the radiant power of the sun.

[[INTRODUCTION]]
The connection between judicial activity and solar power generation may initially seem as mismatched as wearing a robe in a courtroom. Yet, it is precisely through probing such curious correlations that we can uncover new insights and shed light on hidden relationships. In this study, we aim to investigate the unexpected link between these two seemingly unrelated variables—namely, the number of judges in New Mexico and the solar energy production in Honduras. While the link may appear about as clear as a cloudy day, our statistical analysis promises to illuminate the connection, much like the sun peeking through the clouds.
The notion of judicial activity impacting solar energy production may seem far-fetched, but as we embark on this rigorous exploration, it becomes apparent that there are more underlying dynamics at play than meet the eye. Beyond their apparent dissimilarity, both factors are deeply entrenched in the intricate frameworks of institutional and environmental influences, making them ripe for investigation. By leveraging datasets from the Bureau of Labor Statistics and the Energy Information Administration, we aim to cast a spotlight on this unusual relationship, and perhaps surprise ourselves and others with the brilliance of our findings. As we dig deeper into the data, we are reminded of the age-old advice to always approach research with an open mind, for you never know when you might stumble upon a solar-powered breakthrough.
Though the trail we are blazing may seem unorthodox, it is precisely in these uncharted territories that new discoveries and insights emerge. Our approach is not unlike the process of harnessing sunlight—a seemingly mundane and constant occurrence, and yet, when channeled and examined with precision, it can reveal dimensions of our world previously unseen. With this investigation, we hope to ignite an intellectual spark that not only piques the interest of researchers and policymakers, but also brings a radiant smile to the faces of those seeking knowledge in unexpected places.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To investigate the intriguing relationship between judicial activity in New Mexico and solar power generation in Honduras, a multifaceted and rigorous approach was undertaken. The study utilized data spanning the period from 2012 to 2021, sourced predominantly from the Bureau of Labor Statistics and the Energy Information Administration. The data collection process involved sifting through a vast expanse of information, akin to combing through a haystack in search of a solar-powered needle.

The first step in this convoluted yet enlightening endeavor involved gathering comprehensive data on judicial activity in New Mexico. This process required meticulous extraction of information regarding the number of judges, court cases, and judicial decisions. Given the vast and labyrinthine nature of legal proceedings, navigating through the intricate web of judicial data resembled traversing a legal maze, with each twist and turn unravelling new insights into the judicial landscape.

Simultaneously, the research team delved into the solar power sector of Honduras, seeking to understand the intricacies of solar energy production. Data on solar panel installations, solar energy output, and environmental factors influencing solar power generation were meticulously scrutinized. The task of disentangling the myriad influences on solar power production felt akin to unravelling the various layers of an onion, each revealing a fresh perspective on the complex web of solar energy dynamics.

With a wealth of data in hand, statistical analyses were performed to tease out the relationship between the number of judges in New Mexico and the solar power output in Honduras. The use of correlation coefficients, regression analyses, and time series modeling served as the compass guiding our journey through the statistical terrain, providing crucial insights into the interconnectedness of these seemingly disparate variables.

Furthermore, robustness checks and sensitivity analyses were conducted to ensure the reliability and validity of the findings. These checks were akin to stress-testing a solar panel to ascertain its resilience in the face of fluctuating environmental conditions, ensuring that the findings remained robust and enduring, much like the endurance of solar technology in the face of varying climatic challenges.

It is important to note that while the research methodology employed a rigorous and systematic approach, a sense of curiosity and open-mindedness permeated the investigative process. This allowed for the serendipitous discovery of unexpected patterns and connections, akin to stumbling upon a hidden solar-powered gem amidst the expanse of data.

In sum, the methodology adopted in this study reflects a blend of methodical precision, tenacious data collection, and a willingness to embrace the unexpected in pursuit of illuminating insights. Much like the harnessing of solar energy, this research endeavor has sought to channel the radiance of data to shed light on the intriguing relationship between judicial activity and solar power generation, aspiring to spark a wave of newfound understanding in the realms of law and energy.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 The number of judges in New Mexico and Solar power generated in Honduras. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Bureau of Larbor Statistics and Energy Information Administration .

For the time period 2012 to 2021, you found a correlation 0.9509355, r-squared of 0.9042784, 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]]
"Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras"

[[ABSTRACT]]
In this research, we delve into the uncharted territory of connecting the seemingly unrelated realms of judicial activity in New Mexico and solar power generation in Honduras. Using data from the Bureau of Labor Statistics and the Energy Information Administration, we embark on a statistical journey to understand the curious correlation between these two disparate factors. Our findings reveal a striking correlation coefficient of 0.9509355 with p < 0.01 for the period spanning 2012 to 2021, shedding light on the perplexing relationship between the number of judges and solar power output. The implications of these unexpected findings leave us both judicially and energetically intrigued, inviting further exploration and prompting a reevaluation of the intersections between law and the radiant power of the sun.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The connection between judicial activity and solar power generation may initially seem as mismatched as wearing a robe in a courtroom. Yet, it is precisely through probing such curious correlations that we can uncover new insights and shed light on hidden relationships. In this study, we aim to investigate the unexpected link between these two seemingly unrelated variables—namely, the number of judges in New Mexico and the solar energy production in Honduras. While the link may appear about as clear as a cloudy day, our statistical analysis promises to illuminate the connection, much like the sun peeking through the clouds.
The notion of judicial activity impacting solar energy production may seem far-fetched, but as we embark on this rigorous exploration, it becomes apparent that there are more underlying dynamics at play than meet the eye. Beyond their apparent dissimilarity, both factors are deeply entrenched in the intricate frameworks of institutional and environmental influences, making them ripe for investigation. By leveraging datasets from the Bureau of Labor Statistics and the Energy Information Administration, we aim to cast a spotlight on this unusual relationship, and perhaps surprise ourselves and others with the brilliance of our findings. As we dig deeper into the data, we are reminded of the age-old advice to always approach research with an open mind, for you never know when you might stumble upon a solar-powered breakthrough.
Though the trail we are blazing may seem unorthodox, it is precisely in these uncharted territories that new discoveries and insights emerge. Our approach is not unlike the process of harnessing sunlight—a seemingly mundane and constant occurrence, and yet, when channeled and examined with precision, it can reveal dimensions of our world previously unseen. With this investigation, we hope to ignite an intellectual spark that not only piques the interest of researchers and policymakers, but also brings a radiant smile to the faces of those seeking knowledge in unexpected places.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The statistical analysis conducted on the relationship between the number of judges in New Mexico and solar power generation in Honduras has unveiled an unexpected and striking correlation. The correlation coefficient computed for the period from 2012 to 2021 revealed a remarkably high value of 0.9509355, indicating a strong positive association between the two variables. Additionally, the coefficient of determination (r-squared) of 0.9042784 highlights that approximately 90.4% of the variability in solar power generation in Honduras can be explained by the variation in the number of judges in New Mexico. This reveals a close relationship, leaving us as pleasantly surprised as stumbling upon an oasis in the desert of statistical analysis.

Moreover, the p-value of less than 0.01 suggests that the observed correlation is statistically significant, further accentuating the robustness and reliability of our findings. The probability of obtaining such a strong association purely by chance is substantially low, akin to stumbling upon a four-leaf clover in a vast field of statistics.

The visual representation of this compelling correlation is presented in Figure 1, where the scatterplot vividly illustrates the tight clustering of data points, affirming the coherence between judicial activity in New Mexico and solar power generation in Honduras. The figure serves as a beacon, guiding us through the maze of statistical analysis with its illuminating evidence of the surprising relationship between these seemingly incongruous variables.

Upon uncovering these findings, we couldn't help but bask in the radiant glory of this correlation, as it casts a brilliant light on the unanticipated interplay between the judiciary and the generation of solar power. These results not only challenge traditional notions of causality but also pique curiosity, inviting us to explore the untrodden paths of statistical inquiry and encouraging a reconsideration of the interconnectedness of the legal and environmental spheres. This unexpected relationship between judicial activity and solar power generation serves as a reminder that, in the realm of statistical analysis, sometimes the most illuminating discoveries are found in the most unexpected places.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 The number of judges in New Mexico and Solar power generated in Honduras. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras"

[[ABSTRACT]]
In this research, we delve into the uncharted territory of connecting the seemingly unrelated realms of judicial activity in New Mexico and solar power generation in Honduras. Using data from the Bureau of Labor Statistics and the Energy Information Administration, we embark on a statistical journey to understand the curious correlation between these two disparate factors. Our findings reveal a striking correlation coefficient of 0.9509355 with p < 0.01 for the period spanning 2012 to 2021, shedding light on the perplexing relationship between the number of judges and solar power output. The implications of these unexpected findings leave us both judicially and energetically intrigued, inviting further exploration and prompting a reevaluation of the intersections between law and the radiant power of the sun.

[[LITERATURE REVIEW]]
The findings presented in this literature review reflect an eclectic array of studies that have probed the connection between seemingly incongruous variables, drawing a tenuous thread between the number of judges in New Mexico and solar energy production in Honduras. Smith et al. (2015) first broached the topic in their seminal work, "Judicial Patterns and Solar Surprises: A Statistical Odyssey." This groundbreaking study laid the foundation for exploring the potential interplay between judicial activity and solar power output, paving the way for subsequent investigations.
Following suit, Doe (2017) contributed to this burgeoning field with "Illuminating Justice: A Comparative Analysis of Judicial Luminosity and Solar Energy Potential" expanding the discourse to encompass not only the sheer quantity of judges but also the radiance they emit, in a figurative sense. Jones (2019) further advanced this line of inquiry in "Sunshine in the Courtroom: Exploring the Influence of Solar Power on Judicial Decision-Making" by investigating the potential reciprocal influence of solar energy on judicial reasoning. These scholarly endeavors provided a solid foundation for us to construct our own empirical analysis, despite the rather sunny disposition of this research topic.
Venturing beyond the academic landscape, a foray into related non-fiction literature reveals an eclectic mix of texts that tangentially touch upon elements of this peculiar correlation. "Solar Power and Legal Empowerment" by Greenleaf (2018) takes a sociological approach to the intersection of solar energy and legal systems, offering a multidisciplinary perspective that illuminates hitherto unexplored aspects of our topic. Additionally, "Judicial Beaming: A Radiant History" by Brightman (2016) dissects the historical context of judicial luminosity, shedding light on the overlooked influence of sunlight on legal proceedings.
On a more imaginative note, the fictional realm also offers curious parallels to our investigation. "The Sun Also Judges" by Hemmingway (1926) exemplifies the potential for literary allusions to our research, painting a picturesque narrative where the sun takes on the role of a discerning judge, casting its warm rays of judgment upon the characters. Similarly, "Solar Scales of Justice" by Christie (1930) weaves a tale of legal intrigue against a backdrop of solar symbolism, hinting at the deeper connections that may underpin our seemingly disparate variables.
In a cinematic context, while there may not be an explicitly titled film on our precise topic of interest, "A Few Good Panels" (1992) and "Solar Justice" (2008) are examples of movies that, when viewed through the lens of creative interpretation, can provide some tangential insights into the overlapping themes of justice and solar energy. These cinematic forays, while not directly related to the scholarly literature landscape, offer a lighthearted parallel to the serious investigation at hand, reminding us that unexpected connections may lurk in the most unlikely places.
As we assimilate this diverse array of literature, it becomes evident that the connections we seek may not always be glaringly apparent, much like the elusive nature of a solar eclipse. Nevertheless, we press onward with enthusiasm, poised to unravel the sunny mysteries that lie at the intersection of judicial activity and solar power generation.

[[RESULTS]]
The statistical analysis conducted on the relationship between the number of judges in New Mexico and solar power generation in Honduras has unveiled an unexpected and striking correlation. The correlation coefficient computed for the period from 2012 to 2021 revealed a remarkably high value of 0.9509355, indicating a strong positive association between the two variables. Additionally, the coefficient of determination (r-squared) of 0.9042784 highlights that approximately 90.4% of the variability in solar power generation in Honduras can be explained by the variation in the number of judges in New Mexico. This reveals a close relationship, leaving us as pleasantly surprised as stumbling upon an oasis in the desert of statistical analysis.
Moreover, the p-value of less than 0.01 suggests that the observed correlation is statistically significant, further accentuating the robustness and reliability of our findings. The probability of obtaining such a strong association purely by chance is substantially low, akin to stumbling upon a four-leaf clover in a vast field of statistics.
The visual representation of this compelling correlation is presented in Figure 1, where the scatterplot vividly illustrates the tight clustering of data points, affirming the coherence between judicial activity in New Mexico and solar power generation in Honduras. The figure serves as a beacon, guiding us through the maze of statistical analysis with its illuminating evidence of the surprising relationship between these seemingly incongruous variables.
Upon uncovering these findings, we couldn't help but bask in the radiant glory of this correlation, as it casts a brilliant light on the unanticipated interplay between the judiciary and the generation of solar power. These results not only challenge traditional notions of causality but also pique curiosity, inviting us to explore the untrodden paths of statistical inquiry and encouraging a reconsideration of the interconnectedness of the legal and environmental spheres. This unexpected relationship between judicial activity and solar power generation serves as a reminder that, in the realm of statistical analysis, sometimes the most illuminating discoveries are found in the most unexpected places.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The results of our analysis robustly support and augment the findings of previous research that examined the curious association between the number of judges in New Mexico and solar power generation in Honduras. The strong positive correlation coefficient we observed not only aligns with the prior work of Smith et al. (2015) and Doe (2017) but also sheds new light on the extent of the relationship. It appears that the influence of judicial activity in New Mexico extends far beyond the confines of its state boundaries and reaches as far as the sun-drenched fields of solar power generation in Honduras. While the initial literature review may have left some skeptics in the dark, our findings undeniably illuminate the tangible link between these ostensibly distant variables.

The statistical rigidity of our results, as indicated by the remarkably low p-value and the substantial r-squared value, stands as a testament to the dependable nature of the identified relationship. It seems the correlation between the number of judges in New Mexico and solar power generated in Honduras is as firm as a gavel striking a bench, leaving little room for doubt. The vibrancy of this association, akin to a burst of sunlight breaking through a stormy sky, challenges conventional assumptions and beckons to scholars and practitioners alike to heed its radiant call.

While some may have initially perceived the connection between judicial activity and solar energy production as a mere flight of fancy, our empirical analysis has unequivocally established the genuine nature of this symbiotic relationship. It is a reminder that, much like the expansive reach of solar radiation, the far-reaching influence of the judiciary can extend to unexpected domains, casting its figurative rays of influence across borders and domains. The scholarly endeavors and creative works that initially appeared fanciful or whimsical in the literature review have, in retrospect, provided uncanny hints of the substantial link we've uncovered, demonstrating the surprising ways in which truth can be colored with shades of whimsy.

As we stand on the precipice of this unforeseen juncture between judicial activity and solar power generation, it is evident that our foray into hitherto uncharted statistical terrain has yielded a crop of findings as bright as the midday sun. These results not only reshape our understanding of the interconnectedness of disparate domains but also beckon us to embark on further expeditions into the territory of unexpected correlations, reminding us that the most profound discoveries often emerge from the most unforeseen juxtapositions.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 The number of judges in New Mexico and Solar power generated in Honduras. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras"

[[ABSTRACT]]
In this research, we delve into the uncharted territory of connecting the seemingly unrelated realms of judicial activity in New Mexico and solar power generation in Honduras. Using data from the Bureau of Labor Statistics and the Energy Information Administration, we embark on a statistical journey to understand the curious correlation between these two disparate factors. Our findings reveal a striking correlation coefficient of 0.9509355 with p < 0.01 for the period spanning 2012 to 2021, shedding light on the perplexing relationship between the number of judges and solar power output. The implications of these unexpected findings leave us both judicially and energetically intrigued, inviting further exploration and prompting a reevaluation of the intersections between law and the radiant power of the sun.

[[INTRDUCTION]]
The connection between judicial activity and solar power generation may initially seem as mismatched as wearing a robe in a courtroom. Yet, it is precisely through probing such curious correlations that we can uncover new insights and shed light on hidden relationships. In this study, we aim to investigate the unexpected link between these two seemingly unrelated variables—namely, the number of judges in New Mexico and the solar energy production in Honduras. While the link may appear about as clear as a cloudy day, our statistical analysis promises to illuminate the connection, much like the sun peeking through the clouds.
The notion of judicial activity impacting solar energy production may seem far-fetched, but as we embark on this rigorous exploration, it becomes apparent that there are more underlying dynamics at play than meet the eye. Beyond their apparent dissimilarity, both factors are deeply entrenched in the intricate frameworks of institutional and environmental influences, making them ripe for investigation. By leveraging datasets from the Bureau of Labor Statistics and the Energy Information Administration, we aim to cast a spotlight on this unusual relationship, and perhaps surprise ourselves and others with the brilliance of our findings. As we dig deeper into the data, we are reminded of the age-old advice to always approach research with an open mind, for you never know when you might stumble upon a solar-powered breakthrough.
Though the trail we are blazing may seem unorthodox, it is precisely in these uncharted territories that new discoveries and insights emerge. Our approach is not unlike the process of harnessing sunlight—a seemingly mundane and constant occurrence, and yet, when channeled and examined with precision, it can reveal dimensions of our world previously unseen. With this investigation, we hope to ignite an intellectual spark that not only piques the interest of researchers and policymakers, but also brings a radiant smile to the faces of those seeking knowledge in unexpected places.

[[RESULTS]]
The statistical analysis conducted on the relationship between the number of judges in New Mexico and solar power generation in Honduras has unveiled an unexpected and striking correlation. The correlation coefficient computed for the period from 2012 to 2021 revealed a remarkably high value of 0.9509355, indicating a strong positive association between the two variables. Additionally, the coefficient of determination (r-squared) of 0.9042784 highlights that approximately 90.4% of the variability in solar power generation in Honduras can be explained by the variation in the number of judges in New Mexico. This reveals a close relationship, leaving us as pleasantly surprised as stumbling upon an oasis in the desert of statistical analysis.
Moreover, the p-value of less than 0.01 suggests that the observed correlation is statistically significant, further accentuating the robustness and reliability of our findings. The probability of obtaining such a strong association purely by chance is substantially low, akin to stumbling upon a four-leaf clover in a vast field of statistics.
The visual representation of this compelling correlation is presented in Figure 1, where the scatterplot vividly illustrates the tight clustering of data points, affirming the coherence between judicial activity in New Mexico and solar power generation in Honduras. The figure serves as a beacon, guiding us through the maze of statistical analysis with its illuminating evidence of the surprising relationship between these seemingly incongruous variables.
Upon uncovering these findings, we couldn't help but bask in the radiant glory of this correlation, as it casts a brilliant light on the unanticipated interplay between the judiciary and the generation of solar power. These results not only challenge traditional notions of causality but also pique curiosity, inviting us to explore the untrodden paths of statistical inquiry and encouraging a reconsideration of the interconnectedness of the legal and environmental spheres. This unexpected relationship between judicial activity and solar power generation serves as a reminder that, in the realm of statistical analysis, sometimes the most illuminating discoveries are found in the most unexpected places.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, the findings of this study have shed an unexpected light on the connection between the number of judges in New Mexico and solar power generation in Honduras. The remarkably high correlation coefficient and statistically significant p-value unearthed in our analysis have left us as pleasantly surprised as finding a "solar-powered" flashlight in the dark. These results challenge conventional wisdom and prompt a reevaluation of the intersections between the legal and environmental realms.

The implications of this unexpected correlation are as wide-reaching as the sun's rays, urging a reexamination of the complex interplay between seemingly disparate factors. While some may find the notion of judges impacting solar power generation as far-fetched as finding a pile of legal briefs at a solar panel installation site, our findings invite further exploration and spark curiosity in unexpected places.

As we wrap up this study, we must acknowledge the need for more research in this intriguing area, which promises to be as illuminating as a solar eclipse. However, for now, we are content in the light of these findings and leave with a smile as radiant as a sunny day in New Mexico.


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 Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Solar Legal Studies


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 Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras. Respond only with the name of a fictional organization.

ChatGPT:

The Consortium for Energy Justice and Environmental Equity


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 Judging Solar Power: The Illuminating Connection Between Judicial Activity in New Mexico and Solar Energy Production in Honduras with an abstract of In this research, we delve into the uncharted territory of connecting the seemingly unrelated realms of judicial activity in New Mexico and solar power generation in Honduras. Using data from the Bureau of Labor Statistics and the Energy Information Administration, we embark on a statistical journey to understand the curious correlation between these two disparate factors. Our findings reveal a striking correlation coefficient of 0.9509355 with p < 0.01 for the period spanning 2012 to 2021, shedding light on the perplexing relationship between the number of judges and solar power output. The implications of these unexpected findings leave us both judicially and energetically intrigued, inviting further exploration and prompting a reevaluation of the intersections between law and the radiant power of the sun.

ChatGPT:

judicial activity New Mexico, solar power generation Honduras, correlation between judges and solar power output, statistical analysis, Bureau of Labor Statistics data, Energy Information Administration data

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



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Data details

The number of judges in New Mexico
Detailed data title: BLS estimate of judges, magistrate judges, and magistrates in New Mexico
Source: Bureau of Larbor Statistics
See what else correlates with The number of judges in New Mexico

Solar power generated in Honduras
Detailed data title: Total solar power generated in Honduras in billion kWh
Source: Energy Information Administration
See what else correlates with Solar power generated in Honduras

Correlation r = 0.9509355 (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 = 0.9042784 (Coefficient of determination)
This means 90.4% of the change in the one variable (i.e., Solar power generated in Honduras) is predictable based on the change in the other (i.e., The number of judges in New Mexico) over the 10 years from 2012 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.4E-5. 0.0000238915298630721350000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.95 in 0.0024% of random cases. Said differently, if you correlated 41,856 random variables You don't actually need 41 thousand variables to find a correlation like this one. You can also correlate variables that are not independent. I do this a lot.

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 9 degrees of freedom, 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.

[ 0.8, 0.99 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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


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

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
2012201320142015201620172018201920202021
The number of judges in New Mexico (Laborers)120110110110300320320340340320
Solar power generated in Honduras (Billion kWh)0.0070.0070.0070.4250.8920.9290.9971.1181.0461.27612




Why this works

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




Try it yourself

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

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

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

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

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

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

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

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

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


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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([120,110,110,110,300,320,320,340,340,320,])
array_2 = np.array([0.007,0.007,0.007,0.425,0.892,0.929,0.997,1.118,1.046,1.27612,])
array_1_name = "The number of judges in New Mexico"
array_2_name = "Solar power generated in Honduras"

# 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. 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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Correlation ID: 1287 · Black Variable ID: 11819 · Red Variable ID: 23721
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