Spurious correlation #4,434 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Truckee, California and the second variable is General Electric's stock price (GE). The chart goes from 2002 to 2023, 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 Air pollution in Truckee, California and the second variable is General Electric's stock price (GE). The chart goes from 2002 to 2023, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As air pollution in Truckee decreased, the demand for air purifiers and related products also went down. This led to a decrease in General Electric's stock price, as the company's air purification division took a hit. With fresher air in Truckee, it seems the market for clean air technology just couldn't stay afloat, leaving GE gasping for stock value. Looks like their shares needed a breath of fresh air, but instead, they got a whiff of financial trouble!

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: [A photo-realistic image] of Truckee, California with a clear blue sky, lush greenery, and a symbolically faint silhouette of a struggling GE stock chart hanging in the air. Enhanced Version: [A photo-realistic image] of the serene Truckee, California landscape, with vibrant green trees, a crystal-clear blue sky, and a subtle silhouette of a descending stock chart floating amidst the fresh air. The image should capture the contrast between the pristine environment and the subtle visual representation of General Electric's struggling stock, conveying the impact of environmental change on the company in a thought-provoking manner. 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: Air pollution in Truckee, California and General Electric's stock price (GE). 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: Cutsy art illustration Whimsical illustration A photo-realistic image Macabre An image that looks like it is a scene from A Disney Movie
ChatGPT: A photo-realistic image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Truckee, California and General Electric's stock price (GE), in the following situation: As air pollution in Truckee decreased, the demand for air purifiers and related products also went down. This led to a decrease in General Electric's stock price, as the company's air purification division took a hit. With fresher air in Truckee, it seems the market for clean air technology just couldn't stay afloat, leaving GE gasping for stock value. Looks like their shares needed a breath of fresh air, but instead, they got a whiff of financial trouble!. Keep the description simple.
ChatGPT: In Truckee, California, the air clears up as pollution decreases. People no longer feel the need for air purifiers and related products. Meanwhile, General Electric's stock price takes a dip, reflecting the impact on the company's air purification division. The cleaner air in Truckee seems to have left GE's shares gasping for a breath of fresh air, but instead, they got a whiff of financial trouble.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A photo-realistic image ] and depicting this scene: In Truckee, California, the air clears up as pollution decreases. People no longer feel the need for air purifiers and related products. Meanwhile, General Electric's stock price takes a dip, reflecting the impact on the company's air purification division. The cleaner air in Truckee seems to have left GE's shares gasping for a breath of fresh air, but instead, they got a whiff of financial trouble. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: [A photo-realistic image] of Truckee, California with a clear blue sky, lush greenery, and a symbolically faint silhouette of a struggling GE stock chart hanging in the air. Enhanced Version: [A photo-realistic image] of the serene Truckee, California landscape, with vibrant green trees, a crystal-clear blue sky, and a subtle silhouette of a descending stock chart floating amidst the fresh air. The image should capture the contrast between the pristine environment and the subtle visual representation of General Electric's struggling stock, conveying the impact of environmental change on the company in a thought-provoking manner.

*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 a decrease in Air pollution in Truckee, California caused General Electric's stock price (GE) to decrease. Make it feel plausible.

AI academic paper

(Because p < 0.01)

A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price

The Journal of Environmental Economics and Equity

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Truckee, California and General Electric's stock price (GE). 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price"

[[ABSTRACT]]
This paper delves into the interplay between air pollution levels in Truckee, California, and the stock price of General Electric (GE) over the period from 2002 to 2023. Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to investigate the potential link between these seemingly disparate factors.

Our findings revealed a compelling correlation coefficient of 0.8133860 between air pollution in Truckee and GE's stock price, indicative of a robust relationship. Furthermore, the obtained p-value of less than 0.01 underscored the statistical significance of this association, lending credence to the notion that air quality in Truckee could indeed exert an influence on GE's stock performance.

Through our research, we venture into uncharted territory, shedding light on the far-reaching impacts of air pollution and its unexpected resonance within the realm of stock market dynamics. Our study sets the stage for further exploration of this unexpected correlation, demonstrating that beneath the surface of seemingly unrelated elements, there may lurk hidden connections waiting to be unearthed.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of environmental factors and financial markets has long been a subject of interest and intrigue. Amidst the cacophony of economic indicators and the ever-shifting winds of environmental concerns, lies a realm where the whims of Mother Nature and the machinations of Wall Street converge. In this milieu, we find the perplexing case of Truckee, California, a place known for its pristine natural beauty and the faint aroma of diesel fumes wafting through the air.
In recent years, the dialogue surrounding air quality has taken center stage, with a growing awareness of the profound impact it exerts on public health and ecological balance. Yet, amidst this discourse, one could be forgiven for overlooking the curious dance between the air pollution levels in Truckee and the stock price of General Electric (GE). Indeed, while it may seem counterintuitive to link the emissions from diesel engines to the ticker symbols flashing on the stock exchange, our research suggests that there may be more to this connection than meets the eye.
As we embark on this investigative journey, it is crucial to recognize the multifaceted nature of the relationship between air quality and stock prices. Our study ventures beyond the conventional realms of environmental science and financial analysis, bridging the gap between these seemingly disparate fields. With a discerning eye for statistical rigor, we seek to unravel the enigma that lurks beneath the surface of this unlikely correlation, with a healthy dose of academic skepticism and a sprinkle of curiosity.
The aim of this paper is to present our findings on the link between air pollution in Truckee, California, and General Electric's stock price, examining the potential implications and shedding light on the intricate web of factors that underpin this association. By doing so, we hope to spark further discussion and inquiry into the broader implications of environmental phenomena on financial markets, reminding ourselves that sometimes, the air we breathe may hold clues to the movements of the market – a breath of fresh AIRE, if you will.
So, dear reader, fasten your seatbelt as we venture into the realm where diesel meets dividends, where smog mingles with stock options, and where the unfolding narrative of air quality intertwines with the price of GE stock. Welcome to a world where the atmosphere in more ways than one impacts the financial atmosphere, where environmental data takes on an unexpected weight in the balance of the market.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The literature on the relationship between environmental factors and stock prices provides a comprehensive backdrop for our investigation. Several studies have examined the impact of air pollution on various aspects of the economy, from public health to economic productivity. Smith et al. (2015) established a compelling link between particulate matter in urban environments and the financial performance of industrial firms. Likewise, Doe and Jones (2018) conducted a meta-analysis of air quality indices and stock market trends, elucidating the nuanced ways in which environmental variables may influence market dynamics.

In "Air Quality and Financial Markets" by Green, the authors delve into the subtleties of how air pollution indicators can serve as early warning signals for shifts in market sentiment, much like the proverbial canary in the coal mine. Shifting our focus to a more unconventional source, in "The Economics of Clean Air," Johnson provides a thorough exploration of the economic implications of air pollution control, offering insights that may resonate with our investigation.

Drawing inspiration from unexpected sources, the fictional works of Dickens in "Bleak House" and Steinbeck's "Grapes of Wrath" offer poignant narratives on the societal impacts of environmental degradation, hinting at the potential reverberations in financial markets. Additionally, the game Monopoly, with its property values impacted by pollution and the whims of chance, serves as a whimsical reminder of the intertwined nature of economics and environmental variables.

While these sources offer valuable insights, none directly address the specific correlation between air pollution in Truckee, California and General Electric's stock price. As we traverse this uncharted territory, we acknowledge the need for a tailored exploration of this unique intersection. With a nod to both empirical evidence and the unexpected, we set the stage for our pioneering analysis of the Diesel Dilemma, where the fumes of Truckee may hold the key to unlocking the enigmatic dance of stock prices.

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 Air pollution in Truckee, California and General Electric's stock price (GE). 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 Environmental Protection Agency and LSEG Analytics (Refinitiv) . You used data from 2002 to 2023

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]]
"A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price"

[[ABSTRACT]]
This paper delves into the interplay between air pollution levels in Truckee, California, and the stock price of General Electric (GE) over the period from 2002 to 2023. Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to investigate the potential link between these seemingly disparate factors.

Our findings revealed a compelling correlation coefficient of 0.8133860 between air pollution in Truckee and GE's stock price, indicative of a robust relationship. Furthermore, the obtained p-value of less than 0.01 underscored the statistical significance of this association, lending credence to the notion that air quality in Truckee could indeed exert an influence on GE's stock performance.

Through our research, we venture into uncharted territory, shedding light on the far-reaching impacts of air pollution and its unexpected resonance within the realm of stock market dynamics. Our study sets the stage for further exploration of this unexpected correlation, demonstrating that beneath the surface of seemingly unrelated elements, there may lurk hidden connections waiting to be unearthed.

[[INTRODUCTION]]
The intersection of environmental factors and financial markets has long been a subject of interest and intrigue. Amidst the cacophony of economic indicators and the ever-shifting winds of environmental concerns, lies a realm where the whims of Mother Nature and the machinations of Wall Street converge. In this milieu, we find the perplexing case of Truckee, California, a place known for its pristine natural beauty and the faint aroma of diesel fumes wafting through the air.
In recent years, the dialogue surrounding air quality has taken center stage, with a growing awareness of the profound impact it exerts on public health and ecological balance. Yet, amidst this discourse, one could be forgiven for overlooking the curious dance between the air pollution levels in Truckee and the stock price of General Electric (GE). Indeed, while it may seem counterintuitive to link the emissions from diesel engines to the ticker symbols flashing on the stock exchange, our research suggests that there may be more to this connection than meets the eye.
As we embark on this investigative journey, it is crucial to recognize the multifaceted nature of the relationship between air quality and stock prices. Our study ventures beyond the conventional realms of environmental science and financial analysis, bridging the gap between these seemingly disparate fields. With a discerning eye for statistical rigor, we seek to unravel the enigma that lurks beneath the surface of this unlikely correlation, with a healthy dose of academic skepticism and a sprinkle of curiosity.
The aim of this paper is to present our findings on the link between air pollution in Truckee, California, and General Electric's stock price, examining the potential implications and shedding light on the intricate web of factors that underpin this association. By doing so, we hope to spark further discussion and inquiry into the broader implications of environmental phenomena on financial markets, reminding ourselves that sometimes, the air we breathe may hold clues to the movements of the market – a breath of fresh AIRE, if you will.
So, dear reader, fasten your seatbelt as we venture into the realm where diesel meets dividends, where smog mingles with stock options, and where the unfolding narrative of air quality intertwines with the price of GE stock. Welcome to a world where the atmosphere in more ways than one impacts the financial atmosphere, where environmental data takes on an unexpected weight in the balance of the market.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In order to untangle the mysterious dance between air pollution levels in Truckee, California, and the stock price of General Electric (GE), a meticulously crafted research methodology was employed. We gathered our data from various sources, primarily the Environmental Protection Agency's air quality monitoring records and the financial treasure trove of LSEG Analytics (Refinitiv). This prodigious gathering of data spans the years from 2002 to 2023, capturing the dynamics of air pollution and stock prices over a substantial period.

To begin our methodical odyssey, we harnessed the power of statistical analysis to scrutinize the relationship between these seemingly incongruent variables. Firstly, we computed descriptive statistics to paint a vivid portrait of the air quality in Truckee, with a particular focus on the levels of diesel particulate matter swirling through the atmosphere. This provided a foundational understanding of the environmental backdrop against which we would evaluate the celestial ascent and descent of GE's stock price.

Next, we delved into the realm of correlation analysis, leveraging the prodigious force of Pearson's correlation coefficient to measure the strength and direction of the relationship between air pollution in Truckee and GE's stock price. After all, what better way to gauge the cosmic alignment of these two entities than through the lens of robust statistical analysis?

Furthermore, in our tireless quest for empirical evidence, we unleashed the formidable power of regression analysis. By concocting intricate mathematical models, we sought to disentangle the intricate web of causality between air pollution levels in Truckee and the fluctuating fortunes of GE's stock price. We can now confidently proclaim that our regression models were so cutting edge, they could even cut through the diesel haze in Truckee itself!

A crucial aspect of our methodology involved conducting hypothesis testing to ascertain the statistical significance of the observed relationship. This involved the careful calculation of p-values, those elusive indicators of whether our findings were merely statistical flukes or genuinely meaningful discoveries. Spoiler alert: our findings were as statistically significant as a mammoth in a room full of house cats.

And for the pièce de résistance of our methodological masterpiece, we employed a time series analysis to discern the temporal patterns and dynamics of air pollution in Truckee and their potential influence on the price trajectory of our beloved GE stock. This allowed us to peer into the swirling mists of time and extract crucial insights into the enduring connection between the air we breathe and the fiscal tides of General Electric's stock.

In summary, our methodology was as robust as a bulldozer and as dazzling as a diamond in the rough, paving the way for a rigorous and enlightening investigation of the enigmatic intersection between air pollution in Truckee and the ebb and flow of GE's stock price. So, dear reader, fasten your seatbelts as we journey through the data-rich landscape where statistical rigor meets financial intrigue, and where the improbable connections between air quality and stock prices are brought to light.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between Air pollution in Truckee, California and General Electric's stock price (GE). 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 Environmental Protection Agency and LSEG Analytics (Refinitiv) .

For the time period 2002 to 2023, you found a correlation 0.8133860, r-squared of 0.6615967, 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]]
"A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price"

[[ABSTRACT]]
This paper delves into the interplay between air pollution levels in Truckee, California, and the stock price of General Electric (GE) over the period from 2002 to 2023. Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to investigate the potential link between these seemingly disparate factors.

Our findings revealed a compelling correlation coefficient of 0.8133860 between air pollution in Truckee and GE's stock price, indicative of a robust relationship. Furthermore, the obtained p-value of less than 0.01 underscored the statistical significance of this association, lending credence to the notion that air quality in Truckee could indeed exert an influence on GE's stock performance.

Through our research, we venture into uncharted territory, shedding light on the far-reaching impacts of air pollution and its unexpected resonance within the realm of stock market dynamics. Our study sets the stage for further exploration of this unexpected correlation, demonstrating that beneath the surface of seemingly unrelated elements, there may lurk hidden connections waiting to be unearthed.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of environmental factors and financial markets has long been a subject of interest and intrigue. Amidst the cacophony of economic indicators and the ever-shifting winds of environmental concerns, lies a realm where the whims of Mother Nature and the machinations of Wall Street converge. In this milieu, we find the perplexing case of Truckee, California, a place known for its pristine natural beauty and the faint aroma of diesel fumes wafting through the air.
In recent years, the dialogue surrounding air quality has taken center stage, with a growing awareness of the profound impact it exerts on public health and ecological balance. Yet, amidst this discourse, one could be forgiven for overlooking the curious dance between the air pollution levels in Truckee and the stock price of General Electric (GE). Indeed, while it may seem counterintuitive to link the emissions from diesel engines to the ticker symbols flashing on the stock exchange, our research suggests that there may be more to this connection than meets the eye.
As we embark on this investigative journey, it is crucial to recognize the multifaceted nature of the relationship between air quality and stock prices. Our study ventures beyond the conventional realms of environmental science and financial analysis, bridging the gap between these seemingly disparate fields. With a discerning eye for statistical rigor, we seek to unravel the enigma that lurks beneath the surface of this unlikely correlation, with a healthy dose of academic skepticism and a sprinkle of curiosity.
The aim of this paper is to present our findings on the link between air pollution in Truckee, California, and General Electric's stock price, examining the potential implications and shedding light on the intricate web of factors that underpin this association. By doing so, we hope to spark further discussion and inquiry into the broader implications of environmental phenomena on financial markets, reminding ourselves that sometimes, the air we breathe may hold clues to the movements of the market – a breath of fresh AIRE, if you will.
So, dear reader, fasten your seatbelt as we venture into the realm where diesel meets dividends, where smog mingles with stock options, and where the unfolding narrative of air quality intertwines with the price of GE stock. Welcome to a world where the atmosphere in more ways than one impacts the financial atmosphere, where environmental data takes on an unexpected weight in the balance of the market.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

Upon conducting our rigorous analysis, we discovered a notably strong positive correlation between air pollution levels in Truckee, California, and General Electric's (GE) stock price over the period from 2002 to 2023. The correlation coefficient of 0.8133860 suggests a robust relationship between these seemingly unrelated variables. This finding was further substantiated by an r-squared value of 0.6615967, indicating that approximately 66.16% of the variability in GE's stock price can be explained by changes in air pollution levels in Truckee. The p-value of less than 0.01 provides compelling evidence of the statistical significance of this correlation, much like finding a diamond in the rough or a breath of fresh AIRE in a crowded room.

(Fig. 1) depicts a scatterplot illustrating the striking correlation between air pollution in Truckee and GE's stock price, affirming our statistical findings with a visual representation. The data points coalesce into a discernible pattern, akin to how diesel fumes may hang in the air on a particularly smoggy day.

With this solid empirical evidence in hand, we can confidently assert that the relationship between air quality in Truckee and GE's stock performance is not merely a flight of fancy, but a substantive and noteworthy connection. This unexpected linkage calls to mind the hidden currents that underlie seemingly unrelated phenomena, reminding us that sometimes, the most unassuming factors can exert a profound influence - much like the humble bumblebee's impact on an entire ecosystem.

Our investigation into the Diesel Dilemma offers a startling revelation, underscoring the need to consider the broader implications of environmental factors on financial markets. As we bask in the glow of this unexpected correlation, we are reminded that the world of data analysis is full of surprises, much like stumbling upon a dollar bill amidst a pile of discarded stock market reports.

In summary, our findings illuminate the unanticipated interplay between air pollution in Truckee, California, and GE's stock price, heralding a new frontier in understanding the intricate interconnections that shape our financial landscape. This research sets the stage for further exploration of the Diesel Dilemma, tantalizing us with the prospect of uncovering hidden linkages amidst the web of data that surrounds us.

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 Air pollution in Truckee, California and General Electric's stock price (GE). 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]]
"A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price"

[[ABSTRACT]]
This paper delves into the interplay between air pollution levels in Truckee, California, and the stock price of General Electric (GE) over the period from 2002 to 2023. Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to investigate the potential link between these seemingly disparate factors.

Our findings revealed a compelling correlation coefficient of 0.8133860 between air pollution in Truckee and GE's stock price, indicative of a robust relationship. Furthermore, the obtained p-value of less than 0.01 underscored the statistical significance of this association, lending credence to the notion that air quality in Truckee could indeed exert an influence on GE's stock performance.

Through our research, we venture into uncharted territory, shedding light on the far-reaching impacts of air pollution and its unexpected resonance within the realm of stock market dynamics. Our study sets the stage for further exploration of this unexpected correlation, demonstrating that beneath the surface of seemingly unrelated elements, there may lurk hidden connections waiting to be unearthed.

[[LITERATURE REVIEW]]
The literature on the relationship between environmental factors and stock prices provides a comprehensive backdrop for our investigation. Several studies have examined the impact of air pollution on various aspects of the economy, from public health to economic productivity. Smith et al. (2015) established a compelling link between particulate matter in urban environments and the financial performance of industrial firms. Likewise, Doe and Jones (2018) conducted a meta-analysis of air quality indices and stock market trends, elucidating the nuanced ways in which environmental variables may influence market dynamics.
In "Air Quality and Financial Markets" by Green, the authors delve into the subtleties of how air pollution indicators can serve as early warning signals for shifts in market sentiment, much like the proverbial canary in the coal mine. Shifting our focus to a more unconventional source, in "The Economics of Clean Air," Johnson provides a thorough exploration of the economic implications of air pollution control, offering insights that may resonate with our investigation.
Drawing inspiration from unexpected sources, the fictional works of Dickens in "Bleak House" and Steinbeck's "Grapes of Wrath" offer poignant narratives on the societal impacts of environmental degradation, hinting at the potential reverberations in financial markets. Additionally, the game Monopoly, with its property values impacted by pollution and the whims of chance, serves as a whimsical reminder of the intertwined nature of economics and environmental variables.
While these sources offer valuable insights, none directly address the specific correlation between air pollution in Truckee, California and General Electric's stock price. As we traverse this uncharted territory, we acknowledge the need for a tailored exploration of this unique intersection. With a nod to both empirical evidence and the unexpected, we set the stage for our pioneering analysis of the Diesel Dilemma, where the fumes of Truckee may hold the key to unlocking the enigmatic dance of stock prices.

[[RESULTS]]
Upon conducting our rigorous analysis, we discovered a notably strong positive correlation between air pollution levels in Truckee, California, and General Electric's (GE) stock price over the period from 2002 to 2023. The correlation coefficient of 0.8133860 suggests a robust relationship between these seemingly unrelated variables. This finding was further substantiated by an r-squared value of 0.6615967, indicating that approximately 66.16% of the variability in GE's stock price can be explained by changes in air pollution levels in Truckee. The p-value of less than 0.01 provides compelling evidence of the statistical significance of this correlation, much like finding a diamond in the rough or a breath of fresh AIRE in a crowded room.
(Fig. 1) depicts a scatterplot illustrating the striking correlation between air pollution in Truckee and GE's stock price, affirming our statistical findings with a visual representation. The data points coalesce into a discernible pattern, akin to how diesel fumes may hang in the air on a particularly smoggy day.
With this solid empirical evidence in hand, we can confidently assert that the relationship between air quality in Truckee and GE's stock performance is not merely a flight of fancy, but a substantive and noteworthy connection. This unexpected linkage calls to mind the hidden currents that underlie seemingly unrelated phenomena, reminding us that sometimes, the most unassuming factors can exert a profound influence - much like the humble bumblebee's impact on an entire ecosystem.
Our investigation into the Diesel Dilemma offers a startling revelation, underscoring the need to consider the broader implications of environmental factors on financial markets. As we bask in the glow of this unexpected correlation, we are reminded that the world of data analysis is full of surprises, much like stumbling upon a dollar bill amidst a pile of discarded stock market reports.
In summary, our findings illuminate the unanticipated interplay between air pollution in Truckee, California, and GE's stock price, heralding a new frontier in understanding the intricate interconnections that shape our financial landscape. This research sets the stage for further exploration of the Diesel Dilemma, tantalizing us with the prospect of uncovering hidden linkages amidst the web of data that surrounds us.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our investigation has unveiled a surprising nexus between air pollution in Truckee, California, and General Electric's stock price, offering compelling evidence of their interconnectedness. This unforeseen correlation aligns with prior research that has explored the uncharted terrain where environmental factors converge with financial dynamics. As we traverse this unconventional landscape, our findings resonate with the groundbreaking work of Smith et al. (2015), who laid the groundwork for understanding how particulate matter in urban settings can reverberate through the financial realm. Much like a particulate in the wind, our results stir up a newfound appreciation for the potential implications of air quality on corporate performance.

Moreover, our discovery echoes the tenets elucidated by Green in "Air Quality and Financial Markets," emphasizing how environmental indicators can serve as barometers for market shifts, much like the barometric pressures that guide a ship through stormy seas. Our analysis serves as a tangible manifestation of these theoretical underpinnings, highlighting the tangible impact of air quality in shaping stock price movements. The metaphorical canary in the coal mine becomes a tangible concept, as the fumes of Truckee offer a poignant reminder of the hidden signals that underlie market sentiment.

In a similar vein, our unorthodox exploration touches upon the whimsical intersection of reality and fiction, drawing parallels to the societal reverberations depicted in Dickens' "Bleak House" and Steinbeck's "Grapes of Wrath." The unexpected ties between environmental degradation and financial markets manifest in our findings, unveiling a relationship that may well be as enduring as the pages that immortalize these somber tales. And while Monopoly may remain a mere game, its portrayal of property values impacted by pollution underscores the tangible realities we have unearthed, proving that sometimes, reality can outdo even the most imaginative of board games.

Our statistical analysis not only affirms the unexpected correlation but also cements the need for further exploration of this unorthodox synergy. The strong correlation coefficient and the statistically significant p-value serve as pillars of evidence, reinforcing the weight of our findings in a manner reminiscent of discovering a nugget of gold in a stream of data. These results prompt a rethinking of the conventional boundaries that demarcate environmental impact and financial performance, inviting us to consider the tangential threads that connect seemingly disparate realms.

As we revel in the revelatory nature of our findings, we are reminded that the world of research is replete with nuggets of surprise and whimsy, much like a treasure hunt unfolding amidst the sea of data points. Our study not only sheds light on the latent connections between air pollution and stock prices but also beckons us to embrace the unexpected with open arms. As we navigate this uncharted nexus, we find ourselves at the precipice of new frontiers, tantalizingly close to the next revelation that lies hidden in plain sight.

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 Air pollution in Truckee, California and General Electric's stock price (GE). 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]]
"A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price"

[[ABSTRACT]]
This paper delves into the interplay between air pollution levels in Truckee, California, and the stock price of General Electric (GE) over the period from 2002 to 2023. Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to investigate the potential link between these seemingly disparate factors.

Our findings revealed a compelling correlation coefficient of 0.8133860 between air pollution in Truckee and GE's stock price, indicative of a robust relationship. Furthermore, the obtained p-value of less than 0.01 underscored the statistical significance of this association, lending credence to the notion that air quality in Truckee could indeed exert an influence on GE's stock performance.

Through our research, we venture into uncharted territory, shedding light on the far-reaching impacts of air pollution and its unexpected resonance within the realm of stock market dynamics. Our study sets the stage for further exploration of this unexpected correlation, demonstrating that beneath the surface of seemingly unrelated elements, there may lurk hidden connections waiting to be unearthed.

[[INTRDUCTION]]
The intersection of environmental factors and financial markets has long been a subject of interest and intrigue. Amidst the cacophony of economic indicators and the ever-shifting winds of environmental concerns, lies a realm where the whims of Mother Nature and the machinations of Wall Street converge. In this milieu, we find the perplexing case of Truckee, California, a place known for its pristine natural beauty and the faint aroma of diesel fumes wafting through the air.
In recent years, the dialogue surrounding air quality has taken center stage, with a growing awareness of the profound impact it exerts on public health and ecological balance. Yet, amidst this discourse, one could be forgiven for overlooking the curious dance between the air pollution levels in Truckee and the stock price of General Electric (GE). Indeed, while it may seem counterintuitive to link the emissions from diesel engines to the ticker symbols flashing on the stock exchange, our research suggests that there may be more to this connection than meets the eye.
As we embark on this investigative journey, it is crucial to recognize the multifaceted nature of the relationship between air quality and stock prices. Our study ventures beyond the conventional realms of environmental science and financial analysis, bridging the gap between these seemingly disparate fields. With a discerning eye for statistical rigor, we seek to unravel the enigma that lurks beneath the surface of this unlikely correlation, with a healthy dose of academic skepticism and a sprinkle of curiosity.
The aim of this paper is to present our findings on the link between air pollution in Truckee, California, and General Electric's stock price, examining the potential implications and shedding light on the intricate web of factors that underpin this association. By doing so, we hope to spark further discussion and inquiry into the broader implications of environmental phenomena on financial markets, reminding ourselves that sometimes, the air we breathe may hold clues to the movements of the market – a breath of fresh AIRE, if you will.
So, dear reader, fasten your seatbelt as we venture into the realm where diesel meets dividends, where smog mingles with stock options, and where the unfolding narrative of air quality intertwines with the price of GE stock. Welcome to a world where the atmosphere in more ways than one impacts the financial atmosphere, where environmental data takes on an unexpected weight in the balance of the market.

[[RESULTS]]
Upon conducting our rigorous analysis, we discovered a notably strong positive correlation between air pollution levels in Truckee, California, and General Electric's (GE) stock price over the period from 2002 to 2023. The correlation coefficient of 0.8133860 suggests a robust relationship between these seemingly unrelated variables. This finding was further substantiated by an r-squared value of 0.6615967, indicating that approximately 66.16% of the variability in GE's stock price can be explained by changes in air pollution levels in Truckee. The p-value of less than 0.01 provides compelling evidence of the statistical significance of this correlation, much like finding a diamond in the rough or a breath of fresh AIRE in a crowded room.
(Fig. 1) depicts a scatterplot illustrating the striking correlation between air pollution in Truckee and GE's stock price, affirming our statistical findings with a visual representation. The data points coalesce into a discernible pattern, akin to how diesel fumes may hang in the air on a particularly smoggy day.
With this solid empirical evidence in hand, we can confidently assert that the relationship between air quality in Truckee and GE's stock performance is not merely a flight of fancy, but a substantive and noteworthy connection. This unexpected linkage calls to mind the hidden currents that underlie seemingly unrelated phenomena, reminding us that sometimes, the most unassuming factors can exert a profound influence - much like the humble bumblebee's impact on an entire ecosystem.
Our investigation into the Diesel Dilemma offers a startling revelation, underscoring the need to consider the broader implications of environmental factors on financial markets. As we bask in the glow of this unexpected correlation, we are reminded that the world of data analysis is full of surprises, much like stumbling upon a dollar bill amidst a pile of discarded stock market reports.
In summary, our findings illuminate the unanticipated interplay between air pollution in Truckee, California, and GE's stock price, heralding a new frontier in understanding the intricate interconnections that shape our financial landscape. This research sets the stage for further exploration of the Diesel Dilemma, tantalizing us with the prospect of uncovering hidden linkages amidst the web of data that surrounds us.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unveiled an intriguing correlation between air pollution in Truckee, California, and General Electric's stock price, highlighting the surprising interdependence between these seemingly disparate elements. The robust correlation coefficient and statistically significant p-value provide compelling evidence of this unexpected connection, leaving us pondering the whims of fate that intertwine the breath of fresh AIRE in Truckee with the fluctuations of stock prices in the market.

As we reflect on our findings, we are reminded that the world of data analysis and research holds a trove of surprises and hidden revelations, much like stumbling upon a pot of gold at the end of a statistical rainbow. The unexpected nature of the correlation between air quality and stock performance illustrates the need for a broader perspective in understanding the multifaceted dynamics that underpin financial markets, prompting us to consider the impact of environmental factors in a new light. After all, sometimes the most profound insights emerge from the most unlikely sources, akin to finding a pearl nestled within an oyster shell.

In light of these findings, we assert that no more research is needed in this particular area. The link we have uncovered between air pollution in Truckee and General Electric's stock price stands as a testament to the serendipitous connections that permeate our world, reminding us to approach our research endeavors with an open mind and a keen eye for unexpected correlations.

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 A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Environmental Economics and Equity

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 A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

Society for Sustainable Transportation 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 A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price with an abstract of This paper delves into the interplay between air pollution levels in Truckee, California, and the stock price of General Electric (GE) over the period from 2002 to 2023. Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to investigate the potential link between these seemingly disparate factors.
Our findings revealed a compelling correlation coefficient of 0.8133860 between air pollution in Truckee and GE's stock price, indicative of a robust relationship. Furthermore, the obtained p-value of less than 0.01 underscored the statistical significance of this association, lending credence to the notion that air quality in Truckee could indeed exert an influence on GE's stock performance.
Through our research, we venture into uncharted territory, shedding light on the far-reaching impacts of air pollution and its unexpected resonance within the realm of stock market dynamics. Our study sets the stage for further exploration of this unexpected correlation, demonstrating that beneath the surface of seemingly unrelated elements, there may lurk hidden connections waiting to be unearthed.

ChatGPT:

Truckee California air quality, General Electric stock price, air pollution impact on stock price, diesel emissions impact on stock market, environmental impact on stock prices, Truckee air pollution correlation with stock prices, linking air quality to stock performance, stock market dynamics and air pollution, impact of environmental factors on stock price, Truckee pollution and General Electric stock.

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

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Data details

Air pollution in Truckee, California
Detailed data title: Percentage of days with moderate or worse air quality in Truckee-Grass Valley, CA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Truckee, California

General Electric's stock price (GE)
Detailed data title: Opening price of General Electric Company (GE) 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 General Electric's stock price (GE)

Correlation r = 0.8133860 (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 = 0.6615967 (Coefficient of determination)
This means 66.2% of the change in the one variable (i.e., General Electric's stock price (GE)) is predictable based on the change in the other (i.e., Air pollution in Truckee, California) over the 22 years from 2002 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.2E-6.^Show 0.0000041728604762368500000000
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 0.81 in 0.00042% of random cases. Said differently, if you correlated 239,644 random variables^Note You don't actually need 239 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 21 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 21 because we have two variables measured over a period of 22 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.

[ 0.6, 0.92 ] 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.

	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Air pollution in Truckee, California (Bad air quality days)	0.50411	0.528767	0.546448	0.484931	0.523288	0.635616	0.486339	0.32967	0.312329	0.312329	0.363388	0.428177	0.413699	0.403846	0.357924	0.452055	0.325069	0.243767	0.26776	0.394521	0.410959	0.237569
General Electric's stock price (GE) (Stock price)	241.83	147.92	186.03	220.29	210.63	224.49	222.63	99.07	91.33	110.96	109.39	129.08	167.18	152.06	183.32	190.05	105.49	44.77	70.11	67.98	74.3	65.59

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([0.50411,0.528767,0.546448,0.484931,0.523288,0.635616,0.486339,0.32967,0.312329,0.312329,0.363388,0.428177,0.413699,0.403846,0.357924,0.452055,0.325069,0.243767,0.26776,0.394521,0.410959,0.237569,])
array_2 = np.array([241.83,147.92,186.03,220.29,210.63,224.49,222.63,99.07,91.33,110.96,109.39,129.08,167.18,152.06,183.32,190.05,105.49,44.77,70.11,67.98,74.3,65.59,])
array_1_name = "Air pollution in Truckee, California"
array_2_name = "General Electric's stock price (GE)"

# 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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Line chart for only Air pollution in Truckee, California
Line chart for only General Electric's stock price (GE)
AI-generated correlation image
The spurious research paper: A Clear Connection: The Diesel Dilemma - Investigating the Impact of Air Quality in Truckee, California on General Electric's Stock Price

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Correlation ID: 4434 · Black Variable ID: 22265 · Red Variable ID: 1615


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