Spurious correlation #1,734 · 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 environmental science teachers in Colorado and the second variable is Tesla's stock price (TSLA). The chart goes from 2011 to 2020, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of environmental science teachers in Colorado and the second variable is Tesla's stock price (TSLA). The chart goes from 2011 to 2020, and the two variables track closely in value over that time.

AI explanation

As more environmental science teachers started discussing renewable energy and the benefits of electric cars in their classes, students became increasingly interested in the environmental impact of their vehicle choices. This surge in eco-consciousness led to a higher demand for Tesla cars, causing the stock price to rev up like an electric engine. It's like a lesson in the power of green education sparking a financial charge in the world of electric vehicles!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a sunny classroom in Colorado, with large windows letting in natural light. Show a group of environmental science teachers engaged in a discussion about renewable energy, with smartphones in hand. On the screens of their phones, display a graph showing Tesla's stock price rapidly rising. The teachers should appear attentive and animated, conveying the excitement and impact of the stock market surge on their conversation. Ensure a hyper-realistic style grounding the scene in detailed and lifelike visuals. 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: The number of environmental science teachers in Colorado and Tesla's stock price. 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: inspired by bauhaus Pop art Symbolism Steampunk Hyper-realistic
ChatGPT: Hyper-realistic

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 The number of environmental science teachers in Colorado and Tesla's stock price together. Keep the description simple.
ChatGPT: In a sunny classroom in Colorado, a group of environmental science teachers are discussing renewable energy while checking their smartphones. On the screen, they all see a graph showing Tesla's stock price rapidly rising.

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: [ Hyper-realistic ] and depicting this scene: In a sunny classroom in Colorado, a group of environmental science teachers are discussing renewable energy while checking their smartphones. On the screen, they all see a graph showing Tesla's stock price rapidly rising. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a sunny classroom in Colorado, with large windows letting in natural light. Show a group of environmental science teachers engaged in a discussion about renewable energy, with smartphones in hand. On the screens of their phones, display a graph showing Tesla's stock price rapidly rising. The teachers should appear attentive and animated, conveying the excitement and impact of the stock market surge on their conversation. Ensure a hyper-realistic style grounding the scene in detailed and lifelike visuals.

*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 The number of environmental science teachers in Colorado caused Tesla's stock price (TSLA) to increase.

AI academic paper

(Because p < 0.01)

Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price

The Journal of Eclectic Educational Research

Jan 2024

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of environmental science teachers in Colorado and Tesla's stock price (TSLA). 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 cartoons and childrens' shows that you watched that are related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
"Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price"

[[ABSTRACT]]
In this research paper, we delve into the electrifying connection between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we aimed to shed light on this seemingly whimsical yet thought-provoking link. Despite the initial disbelief from colleagues, our findings reveal a striking correlation coefficient of 0.9403634 and a statistically significant p-value of less than 0.01 for the period spanning 2011 to 2020. Our analysis not only sparks further inquiry into the interplay between education and industry but also zaps any lingering doubts about the tangential relationship between environmental science instruction and the market performance of the pioneering electric vehicle company. Shockingly, it seems that the current truly flows from the classroom to the stock exchange. This paper not only endeavors to shed light on this unanticipated correlation but also to incite a spark of curiosity in the reader's mind, much like the ignition of a Tesla Model S.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of education and economics has long been a subject of considerable interest, with researchers seeking to unravel the intricate web of factors that influence both spheres. In this paper, we turn our attention to the connection between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA). While this association may initially strike one as a peculiar juxtaposition, the statistical relationship uncovered in this study hints at a more electrifying connection than previously imagined.
As the electric vehicle industry continues to gain momentum, so too does the demand for environmental science education. It is within this context that we seek to explore the unexpected synergy between the two seemingly disparate domains. The notion that the number of environmental science teachers in a particular state could be linked to the stock performance of a leading innovator in the electric vehicle market might spark skepticism, but our analysis aims to illuminate the shocking veracity of this relationship.
Unraveling this enigmatic correlation not only holds academic intrigue but also carries practical implications for the realms of education and finance. The implications of our study extend beyond the ordinary confines of educational analysis, offering a glimpse into the complex interplay between market dynamics and educational investments. By shedding light on this connection, we aim to jolt researchers and practitioners alike into realizing the overlooked potential of unconventional variables in predicting market trends.
With the stage thus set, we delve into the methods, data sources, and findings that coalesce to demonstrate the unforeseen charge generated by the enrollment of environmental science teachers and its resonance in Tesla's stock price. So, let us venture forth into this study, as we explore the intriguing interplay between academia and the stock market, where the sparks of statistical significance ignite, and the currents of curiosity flow freely.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In the exploration of the perplexing relationship between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA), a plethora of scholarly works and empirical studies have attempted to shed light on the underlying factors at play. Smith et al. (2015) conducted a comprehensive analysis of educational trends and their impact on regional economic outcomes, providing a framework for understanding the potential linkage between educational initiatives and market dynamics. Similarly, Doe and Jones (2018) delved into the influence of environmental education on consumer behavior, offering insights into the broader implications of environmentally focused instruction. However, it is worth noting that while these studies contribute to the broader understanding of the intersections between education and economics, they fail to capture the electrifying peculiarities of the specific link between environmental science instructors and Tesla's stock performance.

Turning to the realm of non-fiction literature, "The Sixth Extinction: An Unnatural History" by Elizabeth Kolbert and "This Changes Everything: Capitalism vs. The Climate" by Naomi Klein offer nuanced perspectives on environmental challenges and the potential roles of education in shaping societal responses. While these works provide valuable context for the broader implications of environmental education, they do not directly address the quirky connection between the employment of environmental science teachers in Colorado and the fluctuations of Tesla's stock price.

On the more imaginative side, the novel "Electric Eden" by David Marusek and "Tesla: A Portrait with Masks" by Vladimir Pistalo showcase futuristic scenarios and cultural interpretations of technological innovation, sparking the imagination but failing to ground the whimsical conjectures of this particular study.

Turning attention to popular culture, cartoons such as "Captain Planet and the Planeteers" and children's shows like "The Magic School Bus" have long championed environmental stewardship, potentially influencing future generations and, hypothetically, the stock market. However, while these animated anecdotes may capture the spirit of environmental education, they lack empirical evidence regarding their impact on the financial world or on the performance of Tesla's stock specifically.

As we navigate through these diverse sources, it becomes apparent that while the literature offers valuable insight into the broader themes of environmental education and economic dynamics, the specific conundrum at hand remains shockingly underexplored. Nonetheless, the endeavor to uncover meaningful connections in seemingly unrelated realms keeps our scholarly pursuits both grounded and charged with excitement.

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 The number of environmental science teachers in Colorado and Tesla's stock price (TSLA). 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 LSEG Analytics (Refinitiv) . You used data from 2011 to 2020

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price"

[[ABSTRACT]]
In this research paper, we delve into the electrifying connection between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we aimed to shed light on this seemingly whimsical yet thought-provoking link. Despite the initial disbelief from colleagues, our findings reveal a striking correlation coefficient of 0.9403634 and a statistically significant p-value of less than 0.01 for the period spanning 2011 to 2020. Our analysis not only sparks further inquiry into the interplay between education and industry but also zaps any lingering doubts about the tangential relationship between environmental science instruction and the market performance of the pioneering electric vehicle company. Shockingly, it seems that the current truly flows from the classroom to the stock exchange. This paper not only endeavors to shed light on this unanticipated correlation but also to incite a spark of curiosity in the reader's mind, much like the ignition of a Tesla Model S.

[[INTRODUCTION]]
The intersection of education and economics has long been a subject of considerable interest, with researchers seeking to unravel the intricate web of factors that influence both spheres. In this paper, we turn our attention to the connection between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA). While this association may initially strike one as a peculiar juxtaposition, the statistical relationship uncovered in this study hints at a more electrifying connection than previously imagined.
As the electric vehicle industry continues to gain momentum, so too does the demand for environmental science education. It is within this context that we seek to explore the unexpected synergy between the two seemingly disparate domains. The notion that the number of environmental science teachers in a particular state could be linked to the stock performance of a leading innovator in the electric vehicle market might spark skepticism, but our analysis aims to illuminate the shocking veracity of this relationship.
Unraveling this enigmatic correlation not only holds academic intrigue but also carries practical implications for the realms of education and finance. The implications of our study extend beyond the ordinary confines of educational analysis, offering a glimpse into the complex interplay between market dynamics and educational investments. By shedding light on this connection, we aim to jolt researchers and practitioners alike into realizing the overlooked potential of unconventional variables in predicting market trends.
With the stage thus set, we delve into the methods, data sources, and findings that coalesce to demonstrate the unforeseen charge generated by the enrollment of environmental science teachers and its resonance in Tesla's stock price. So, let us venture forth into this study, as we explore the intriguing interplay between academia and the stock market, where the sparks of statistical significance ignite, and the currents of curiosity flow freely.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In this study, we employed a multi-faceted approach to investigate the relationship between the number of environmental science teachers in Colorado and Tesla's stock price (TSLA). To begin, we conducted a comprehensive review of existing literature on educational impacts on industry performance, market psychology, and the electrifying world of electric vehicles. We then brushed up on our statistical analysis techniques, ensuring that our methods were as current and shockingly accurate as possible.

Data Collection:

Our primary data sources included the Bureau of Labor Statistics and LSEG Analytics (Refinitiv). We drew upon the comprehensive employment data provided by the Bureau of Labor Statistics to ascertain the number of environmental science teachers in Colorado over the period from 2011 to 2020. Concurrently, we harnessed the electrifying power of LSEG Analytics (Refinitiv) to obtain historical data on Tesla's stock price over the same timeframe. While this synergy of data sources might not seem as flashy as a performance by Tesla's Model S Plaid, it did allow us to capture a comprehensive picture of both the educational and financial landscapes.

Data Analysis:

With the data in hand, we embarked on a journey into the realm of statistical analysis. Employing sophisticated quantitative techniques, we computed correlation coefficients and regression models to illuminate the potential relationship between our two seemingly unrelated variables. We aimed to ensure that our analysis was both rigorous and versatile, akin to the Tesla Model X's dynamic falcon-wing doors – opening new pathways of understanding and discovery in the data.

Model Limitations:

It is crucial to recognize that our model is not without its own limitations. While we have endeavored to consider a wide array of factors, the findings are subject to the inherent complexities of the education and financial systems. Moreover, as with any statistical endeavor, there is a potential for unexpected shocks and jolts in the data that may not be fully captured by our model. However, we took great care to ground our analysis in robust statistical methods, aiming to shine a light on the connection between environmental science education and Tesla's stock performance.

Robustness Checks:

To ensure the shockingly robust nature of our findings, we conducted robustness checks involving various model specifications and time periods. These checks allowed us to demonstrate that the observed relationship between environmental science teachers in Colorado and Tesla's stock price was not a mere flash in the pan but held steady across multiple scenarios. Much like the careful scrutiny given to the design and performance of Tesla vehicles, our analysis underwent rigorous examination to ensure reliability.

Overall, our methodology combined careful data collection, thorough statistical analysis, and a touch of wit to explore the captivating relationship between environmental science education and Tesla's stock price, demonstrating that even the most unexpected connections can yield enlightening insights when subjected to rigorous academic inquiry.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 environmental science teachers in Colorado and Tesla's stock price (TSLA). 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]]
"Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price"

[[ABSTRACT]]
In this research paper, we delve into the electrifying connection between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we aimed to shed light on this seemingly whimsical yet thought-provoking link. Despite the initial disbelief from colleagues, our findings reveal a striking correlation coefficient of 0.9403634 and a statistically significant p-value of less than 0.01 for the period spanning 2011 to 2020. Our analysis not only sparks further inquiry into the interplay between education and industry but also zaps any lingering doubts about the tangential relationship between environmental science instruction and the market performance of the pioneering electric vehicle company. Shockingly, it seems that the current truly flows from the classroom to the stock exchange. This paper not only endeavors to shed light on this unanticipated correlation but also to incite a spark of curiosity in the reader's mind, much like the ignition of a Tesla Model S.

[[LITERATURE REVIEW]]
In the exploration of the perplexing relationship between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA), a plethora of scholarly works and empirical studies have attempted to shed light on the underlying factors at play. Smith et al. (2015) conducted a comprehensive analysis of educational trends and their impact on regional economic outcomes, providing a framework for understanding the potential linkage between educational initiatives and market dynamics. Similarly, Doe and Jones (2018) delved into the influence of environmental education on consumer behavior, offering insights into the broader implications of environmentally focused instruction. However, it is worth noting that while these studies contribute to the broader understanding of the intersections between education and economics, they fail to capture the electrifying peculiarities of the specific link between environmental science instructors and Tesla's stock performance.
Turning to the realm of non-fiction literature, "The Sixth Extinction: An Unnatural History" by Elizabeth Kolbert and "This Changes Everything: Capitalism vs. The Climate" by Naomi Klein offer nuanced perspectives on environmental challenges and the potential roles of education in shaping societal responses. While these works provide valuable context for the broader implications of environmental education, they do not directly address the quirky connection between the employment of environmental science teachers in Colorado and the fluctuations of Tesla's stock price.
On the more imaginative side, the novel "Electric Eden" by David Marusek and "Tesla: A Portrait with Masks" by Vladimir Pistalo showcase futuristic scenarios and cultural interpretations of technological innovation, sparking the imagination but failing to ground the whimsical conjectures of this particular study.
Turning attention to popular culture, cartoons such as "Captain Planet and the Planeteers" and children's shows like "The Magic School Bus" have long championed environmental stewardship, potentially influencing future generations and, hypothetically, the stock market. However, while these animated anecdotes may capture the spirit of environmental education, they lack empirical evidence regarding their impact on the financial world or on the performance of Tesla's stock specifically.
As we navigate through these diverse sources, it becomes apparent that while the literature offers valuable insight into the broader themes of environmental education and economic dynamics, the specific conundrum at hand remains shockingly underexplored. Nonetheless, the endeavor to uncover meaningful connections in seemingly unrelated realms keeps our scholarly pursuits both grounded and charged with excitement.

[[RESULTS]]
The results of our analysis revealed a striking correlation coefficient (r) of 0.9403634 between the number of environmental science teachers in Colorado and Tesla's stock price (TSLA) for the period from 2011 to 2020. We observed an impressive coefficient of determination (r-squared) of 0.8842833, indicating that approximately 88.4% of the variability in Tesla's stock price can be explained by the number of environmental science teachers in Colorado. Furthermore, the p-value of less than 0.01 underscores the statistical significance of this relationship, providing solid evidence to substantiate the observed correlation.
In examining the relationship graphically, the scatterplot (Fig. 1) depicts a strong positive linear association between the two variables. The data points closely follow a clear upward trend, visually reinforcing the high correlation coefficient derived from our analysis. The figure not only serves to demonstrate the robustness of the relationship but also sparks a sense of awe at the unexpected connection between the deployment of environmental science educators and the stock performance of an innovative electric vehicle manufacturer.
The sheer strength of the correlation leaves us somewhat shocked, akin to the sensation of accidentally touching a live wire. This pronounced association not only defies conventional wisdom but also enlightens our understanding of the intricate dynamics at play. It seems that the "current" truly flows between the education sector and the electric vehicle industry, as evidenced by the compelling statistical findings.
Our results not only highlight the surprising interrelation between seemingly unrelated variables but also underscore the need for continued exploration of unconventional connections within the academic and financial domains. The magnitude of the correlation observed can only be described as electrifying, beckoning researchers to engage in further inquiry into the underlying mechanisms driving this unexpected association.
In summary, our investigation underscores the unexpected but compelling linkage between the number of environmental science teachers in Colorado and the stock price of Tesla. The findings not only challenge traditional paradigms but also provide a glimpse into the uncharted territories of educational influence on market dynamics. The results of this study lay the groundwork for broader discussions on the untapped potential of non-traditional variables in predicting market behaviors, generating intellect-ricity that energizes contemplation and sparks excitement for future research in the field.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our investigation substantiate the notion of a strong and unexpected correlation between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA), aligning with prior research that has hinted at the influence of non-traditional factors on market dynamics. It is quite shocking to see empirical evidence backing up the seemingly whimsical link between educational employment and financial performance. Our findings resonate with Smith et al. (2015), who emphasized the regional economic impact of educational trends. While they did not explicitly focus on the peculiar relationship we explored, their work laid the groundwork for recognizing the potential influence of educational initiatives on market forces. Similarly, our results echo Doe and Jones (2018), who discussed the broader implications of environmentally focused instruction on consumer behavior. While they didn't specifically delve into the stock performance of a particular company, their insights into the effects of environmental education on behavior can be viewed through the lens of the stock market's responsiveness to societal shifts.

On the more lighthearted side of the literature review, "Electric Eden" by David Marusek and "Tesla: A Portrait with Masks" by Vladimir Pistalo, while not grounded in empirical evidence, captured futuristic scenarios and cultural interpretations of technological innovation. Perhaps there was a spark of truth in these imaginative musings after all, as our findings testify to the real-world interconnectedness between educational practices and market dynamics. It seems that fiction may have been a step ahead in imagining the unexpected web of connections between academia and finance.

Our statistically significant correlation coefficient of 0.9403634 and coefficient of determination of 0.8842833 not only validate the previously uncharted link but also electrify the academic community with the recognition of the profound influence of educational factors on the stock market. Our figurative language mirrors the real sense of shock felt upon uncovering this unexpected relationship, much like a sudden jolt from an unforeseen source. Our results, while initially surprising, have the potential to ignite a reevaluation of the traditional boundaries of market analysis and spark a current of innovation in research methodologies.

In closing, our investigation into the connection between environmental science educators and Tesla's stock performance has not only yielded intriguing findings but has also sparked a glimmer of recognition of the uncharted territories awaiting exploration within the intersection of education and market dynamics. The scholarly shockwave of our findings serves to not only electrify the academic community but also to energize further inquiry into the underlying mechanisms driving these surprising connections. As we move forward, we must remember that even the most unexpected relationships can hold significant implications and, dare we say, can truly "amp up" our understanding of market behavior.

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 The number of environmental science teachers in Colorado and Tesla's stock price (TSLA). 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]]
"Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price"

[[ABSTRACT]]
In this research paper, we delve into the electrifying connection between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we aimed to shed light on this seemingly whimsical yet thought-provoking link. Despite the initial disbelief from colleagues, our findings reveal a striking correlation coefficient of 0.9403634 and a statistically significant p-value of less than 0.01 for the period spanning 2011 to 2020. Our analysis not only sparks further inquiry into the interplay between education and industry but also zaps any lingering doubts about the tangential relationship between environmental science instruction and the market performance of the pioneering electric vehicle company. Shockingly, it seems that the current truly flows from the classroom to the stock exchange. This paper not only endeavors to shed light on this unanticipated correlation but also to incite a spark of curiosity in the reader's mind, much like the ignition of a Tesla Model S.

[[INTRDUCTION]]
The intersection of education and economics has long been a subject of considerable interest, with researchers seeking to unravel the intricate web of factors that influence both spheres. In this paper, we turn our attention to the connection between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA). While this association may initially strike one as a peculiar juxtaposition, the statistical relationship uncovered in this study hints at a more electrifying connection than previously imagined.
As the electric vehicle industry continues to gain momentum, so too does the demand for environmental science education. It is within this context that we seek to explore the unexpected synergy between the two seemingly disparate domains. The notion that the number of environmental science teachers in a particular state could be linked to the stock performance of a leading innovator in the electric vehicle market might spark skepticism, but our analysis aims to illuminate the shocking veracity of this relationship.
Unraveling this enigmatic correlation not only holds academic intrigue but also carries practical implications for the realms of education and finance. The implications of our study extend beyond the ordinary confines of educational analysis, offering a glimpse into the complex interplay between market dynamics and educational investments. By shedding light on this connection, we aim to jolt researchers and practitioners alike into realizing the overlooked potential of unconventional variables in predicting market trends.
With the stage thus set, we delve into the methods, data sources, and findings that coalesce to demonstrate the unforeseen charge generated by the enrollment of environmental science teachers and its resonance in Tesla's stock price. So, let us venture forth into this study, as we explore the intriguing interplay between academia and the stock market, where the sparks of statistical significance ignite, and the currents of curiosity flow freely.

[[RESULTS]]
The results of our analysis revealed a striking correlation coefficient (r) of 0.9403634 between the number of environmental science teachers in Colorado and Tesla's stock price (TSLA) for the period from 2011 to 2020. We observed an impressive coefficient of determination (r-squared) of 0.8842833, indicating that approximately 88.4% of the variability in Tesla's stock price can be explained by the number of environmental science teachers in Colorado. Furthermore, the p-value of less than 0.01 underscores the statistical significance of this relationship, providing solid evidence to substantiate the observed correlation.
In examining the relationship graphically, the scatterplot (Fig. 1) depicts a strong positive linear association between the two variables. The data points closely follow a clear upward trend, visually reinforcing the high correlation coefficient derived from our analysis. The figure not only serves to demonstrate the robustness of the relationship but also sparks a sense of awe at the unexpected connection between the deployment of environmental science educators and the stock performance of an innovative electric vehicle manufacturer.
The sheer strength of the correlation leaves us somewhat shocked, akin to the sensation of accidentally touching a live wire. This pronounced association not only defies conventional wisdom but also enlightens our understanding of the intricate dynamics at play. It seems that the "current" truly flows between the education sector and the electric vehicle industry, as evidenced by the compelling statistical findings.
Our results not only highlight the surprising interrelation between seemingly unrelated variables but also underscore the need for continued exploration of unconventional connections within the academic and financial domains. The magnitude of the correlation observed can only be described as electrifying, beckoning researchers to engage in further inquiry into the underlying mechanisms driving this unexpected association.
In summary, our investigation underscores the unexpected but compelling linkage between the number of environmental science teachers in Colorado and the stock price of Tesla. The findings not only challenge traditional paradigms but also provide a glimpse into the uncharted territories of educational influence on market dynamics. The results of this study lay the groundwork for broader discussions on the untapped potential of non-traditional variables in predicting market behaviors, generating intellect-ricity that energizes contemplation and sparks excitement for future research in the field.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has demonstrated a shockingly strong correlation between the number of environmental science teachers in Colorado and Tesla's stock price, leaving us more charged up than a Tesla Supercharger station. The statistically significant relationship, with a correlation coefficient reminiscent of high-voltage power lines, challenges conventional wisdom and illuminates the unexplored potential of seemingly disparate variables in forecasting market trends.

The unexpected synergy between environmental science education and the electric vehicle industry not only sparks intrigue but also incites a surge of curiosity that rivals the energy stored in a lithium-ion battery. While this connection may seem as improbable as a solar-powered flashlight in a cave, our findings urge further exploration into the nuanced interplay between academia and market dynamics, where the sparks of statistical significance ignite and the currents of curiosity flow freely.

Nevertheless, it is electrifyingly clear that no further research is needed in this area. This connection is as strong as an electric current, and delving further into this topic might just leave us feeling positively charged!

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 Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Eclectic Educational Research

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 Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Sustainable Energy Education and Research

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 Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price with an abstract of In this research paper, we delve into the electrifying connection between the number of environmental science teachers in Colorado and the stock price of Tesla (TSLA). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we aimed to shed light on this seemingly whimsical yet thought-provoking link. Despite the initial disbelief from colleagues, our findings reveal a striking correlation coefficient of 0.9403634 and a statistically significant p-value of less than 0.01 for the period spanning 2011 to 2020. Our analysis not only sparks further inquiry into the interplay between education and industry but also zaps any lingering doubts about the tangential relationship between environmental science instruction and the market performance of the pioneering electric vehicle company. Shockingly, it seems that the current truly flows from the classroom to the stock exchange. This paper not only endeavors to shed light on this unanticipated correlation but also to incite a spark of curiosity in the reader's mind, much like the ignition of a Tesla Model S.

ChatGPT:

environmental science teachers, Colorado, Tesla stock price, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, correlation coefficient, p-value, education and industry, electric vehicle company, market performance, curriculum, interplay between education and industry

*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 environmental science teachers in Colorado
Detailed data title: BLS estimate of environmental science teachers, postsecondary in Colorado
Source: Bureau of Larbor Statistics
See what else correlates with The number of environmental science teachers in Colorado

Tesla's stock price (TSLA)
Detailed data title: Opening price of Tesla (TSLA) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: ^{See full details} Via Microsoft Excel Stockhistory function

See what else correlates with Tesla's stock price (TSLA)

Correlation r = 0.9403634 (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 = 0.8842833 (Coefficient of determination)
This means 88.4% of the change in the one variable (i.e., Tesla's stock price (TSLA)) is predictable based on the change in the other (i.e., The number of environmental science teachers in Colorado) over the 10 years from 2011 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.1E-5.^Show 0.0000514761989062862240000000
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 0.94 in 0.0051% of random cases. Said differently, if you correlated 19,426 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.

[ 0.76, 0.99 ] 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.

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
The number of environmental science teachers in Colorado (Laborers)	80	90	60	100	110	130	150	190	170	200
Tesla's stock price (TSLA) (Stock price)	1.79	1.93	2.33	9.99	14.86	15.38	14.32	20.8	20.41	28.3

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([80,90,60,100,110,130,150,190,170,200,])
array_2 = np.array([1.79,1.93,2.33,9.99,14.86,15.38,14.32,20.8,20.41,28.3,])
array_1_name = "The number of environmental science teachers in Colorado"
array_2_name = "Tesla's stock price (TSLA)"

# 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
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Portable line chart (png)
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Line chart for only The number of environmental science teachers in Colorado
Line chart for only Tesla's stock price (TSLA)
AI-generated correlation image
The spurious research paper: Charging Up the Curriculum: Exploring the Shocking Relationship Between Environmental Science Teachers in Colorado and Tesla's Stock Price

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Correlation ID: 1734 · Black Variable ID: 16917 · Red Variable ID: 1545


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