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Spurious correlation #3,996 · 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 Phoenix and the second variable is ORIX Corporation's stock price (IX).  The chart goes from 2002 to 2023, and the two variables track closely in value over that time. Small Image

AI explanation

As Air pollution in Phoenix increased, more people sought refuge indoors, leading to a surge in demand for ORIX office spaces and driving up the company's stock price. You could say ORIX stock was truly 'air'-rising in value as pollution levels soared.

Model: dalle-3
Prompt: Generate an image of a high-tech stock exchange floor in Phoenix, bustling with activity under clear blue skies. The sleek, modern office is flooded with natural light through floor-to-ceiling windows, offering a panoramic view of the city. The ORIX Corporation's stock price ticker tape is prominently displayed in the center, illuminated by digital screens projecting green numbers indicating a significant surge. Excited investors are depicted in sharp, futuristic attire, enthusiastically high-fiving and cheering against a backdrop of clean, minimalist décor. The scene portrays a fresh, optimistic atmosphere intertwining with the bright, open skies of Phoenix, capturing the essence of high-tech finance amidst a city known for its natural beauty. 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 Phoenix and ORIX Corporation's stock price (IX). 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: High-tech Psychedelic Surrealism Data cyber art Steampunk
ChatGPT: High-tech

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 Phoenix and ORIX Corporation's stock price (IX), in the following situation: As air pollution in Phoenix cleared up, ORIX Corporation's stock price soared because investors finally saw the company as a breath of fresh air in the financial world. The cleaner air symbolized a clean slate for the company, leading to increased confidence and investment, propelling the stock price to new heights.. Keep the description simple.
ChatGPT: Amidst the clear blue skies of Phoenix, a bustling stock exchange office is brimming with excitement. The ORIX Corporation's stock price ticker tape is ablaze with green numbers, indicating a significant surge. Investors are seen high-fiving and cheering as they bask in the newfound success, linking the clean air of the city with the fresh optimism surrounding the company's stock.

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: [ High-tech ] and depicting this scene: Amidst the clear blue skies of Phoenix, a bustling stock exchange office is brimming with excitement. The ORIX Corporation's stock price ticker tape is ablaze with green numbers, indicating a significant surge. Investors are seen high-fiving and cheering as they bask in the newfound success, linking the clean air of the city with the fresh optimism surrounding the company's stock. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a high-tech stock exchange floor in Phoenix, bustling with activity under clear blue skies. The sleek, modern office is flooded with natural light through floor-to-ceiling windows, offering a panoramic view of the city. The ORIX Corporation's stock price ticker tape is prominently displayed in the center, illuminated by digital screens projecting green numbers indicating a significant surge. Excited investors are depicted in sharp, futuristic attire, enthusiastically high-fiving and cheering against a backdrop of clean, minimalist décor. The scene portrays a fresh, optimistic atmosphere intertwining with the bright, open skies of Phoenix, capturing the essence of high-tech finance amidst a city known for its natural beauty.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.

Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how increases in Air pollution in Phoenix positively influenced ORIX Corporation's stock price (IX). Include one good pun.

AI academic paper

(Because p < 0.01)
Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price

The Journal of Atmospheric Economics and Environmental Finance

Jan 2024
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 researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Air pollution in Phoenix and ORIX Corporation's stock price (IX). Make lots of jokes, goofy observations, and puns.

Make the title punny if you can come up with clever pun. If not, make it sound serious.

Your research team used data from Environmental Protection Agency and LSEG Analytics (Refinitiv) to assess this nagging question. You found a correlation coefficient of 0.6449322 and p < 0.01 for 2002 to 2023.

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

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price

[[ABSTRACT]]
In this study, we dive into the atmospheric abyss to examine the uncharted territory of air pollution's influence on stock prices. While most financial analysts focus on traditional market indicators, our research takes a breath of fresh air by exploring the impact of air quality on stock performance. Through rigorous analysis of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we uncovered a surprising connection between the hazy skies of Phoenix and the fluctuating stock price of ORIX Corporation (IX). Our findings reveal a statistically significant correlation coefficient of 0.6449322 and p < 0.01 for the period spanning 2002 to 2023. Prepare to be blown away as we unravel the mysterious interplay between pollution particles and stock market percolations, shedding light on a relationship that's more than just hot air.


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

Please make up the introduction section of an academic research paper presenting the findings of the connection between Air pollution in Phoenix and ORIX Corporation's stock price (IX). Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price

[[ABSTRACT]]
In this study, we dive into the atmospheric abyss to examine the uncharted territory of air pollution's influence on stock prices. While most financial analysts focus on traditional market indicators, our research takes a breath of fresh air by exploring the impact of air quality on stock performance. Through rigorous analysis of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we uncovered a surprising connection between the hazy skies of Phoenix and the fluctuating stock price of ORIX Corporation (IX). Our findings reveal a statistically significant correlation coefficient of 0.6449322 and p < 0.01 for the period spanning 2002 to 2023. Prepare to be blown away as we unravel the mysterious interplay between pollution particles and stock market percolations, shedding light on a relationship that's more than just hot air.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
In the grand symphony of economic analyses and market evaluations, one often overlooks the ethereal melodies carried by the winds of air pollution. While conventional economic models may seem as clear as a crisply laundered lab coat, there exists a hazy frontier of financial dynamics that can truly take your breath away. As we wade into the foggy depths of this uncharted territory, we uncover a tale of two spheres colliding in a dance of uncertainty and volatility.

As financial enthusiasts, we are ever in pursuit of understanding the intricate dance of the stock market, diligently sifting through mountains of data like treasure hunters in a sea of numbers. However, in our quest for monetary enlightenment, we sometimes forget to take a deep breath and consider the larger atmospheric forces at play. In this study, we aim to unravel the enigmatic relationship between air pollution in Phoenix and the stock price of ORIX Corporation (IX), combining the esoteric arts of smogonomics with the age-old dance of supply and demand.

While the mere mention of air pollution may conjure images of ominous plumes and disgruntled trees, we bring a fresh perspective to this atmospheric imbroglio. Our journey takes us through the swirling mists of Phoenix—a city known for its blazing sunsets and, more recently, its perplexing influence on financial barometers. Partnering with the Environmental Protection Agency and LSEG Analytics (Refinitiv), we embark on a quest through data thickets and statistical thickets, armed with our trusty compass of hypothesis testing and a keen sense of scientific curiosity.

The serendipitous discoveries that scatter our path are enough to send any statistician's heart aflutter. Brace yourself, for we have unearthed a statistically significant correlation coefficient of 0.6449322, coupled with a p-value that would make even the most stoic of researchers raise an eyebrow—all spanning the eventful period from 2002 to 2023. Much like a chemical reaction in the lab, this empirical linkage defies the conventional wisdom that stock prices are cast solely from the mold of financial statements and market trends.

But what tantalizing forces are at play in this hazy waltz of stock prices and pollutants? Is it the interplay of nitrogen oxides and market pressures? Could it be the flirtatious dance of sulfur compounds and investor sentiment? We invite you to join us in peeling back the layers of this atmospheric intrigue, to probe the smoggy enigma that lies beneath the surface of financial indices and market reports.

So, let us embark on this whimsical odyssey and delve into the nebulous alchemy of smogonomics, where air pollution and stock prices twirl together in an unexpected duet of numbers and particulates. Prepare to be enthralled, for we are about to uncover a relationship that's more than just hot air—it's a symphony of eternally intertwined variables that hold the potential to surprise, stupefy, and perhaps even amuse the most discerning of economic intellects.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Phoenix and ORIX Corporation's stock price (IX). Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price

[[ABSTRACT]]
In this study, we dive into the atmospheric abyss to examine the uncharted territory of air pollution's influence on stock prices. While most financial analysts focus on traditional market indicators, our research takes a breath of fresh air by exploring the impact of air quality on stock performance. Through rigorous analysis of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we uncovered a surprising connection between the hazy skies of Phoenix and the fluctuating stock price of ORIX Corporation (IX). Our findings reveal a statistically significant correlation coefficient of 0.6449322 and p < 0.01 for the period spanning 2002 to 2023. Prepare to be blown away as we unravel the mysterious interplay between pollution particles and stock market percolations, shedding light on a relationship that's more than just hot air.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the grand symphony of economic analyses and market evaluations, one often overlooks the ethereal melodies carried by the winds of air pollution. While conventional economic models may seem as clear as a crisply laundered lab coat, there exists a hazy frontier of financial dynamics that can truly take your breath away. As we wade into the foggy depths of this uncharted territory, we uncover a tale of two spheres colliding in a dance of uncertainty and volatility.
As financial enthusiasts, we are ever in pursuit of understanding the intricate dance of the stock market, diligently sifting through mountains of data like treasure hunters in a sea of numbers. However, in our quest for monetary enlightenment, we sometimes forget to take a deep breath and consider the larger atmospheric forces at play. In this study, we aim to unravel the enigmatic relationship between air pollution in Phoenix and the stock price of ORIX Corporation (IX), combining the esoteric arts of smogonomics with the age-old dance of supply and demand.
While the mere mention of air pollution may conjure images of ominous plumes and disgruntled trees, we bring a fresh perspective to this atmospheric imbroglio. Our journey takes us through the swirling mists of Phoenix—a city known for its blazing sunsets and, more recently, its perplexing influence on financial barometers. Partnering with the Environmental Protection Agency and LSEG Analytics (Refinitiv), we embark on a quest through data thickets and statistical thickets, armed with our trusty compass of hypothesis testing and a keen sense of scientific curiosity.
The serendipitous discoveries that scatter our path are enough to send any statistician's heart aflutter. Brace yourself, for we have unearthed a statistically significant correlation coefficient of 0.6449322, coupled with a p-value that would make even the most stoic of researchers raise an eyebrow—all spanning the eventful period from 2002 to 2023. Much like a chemical reaction in the lab, this empirical linkage defies the conventional wisdom that stock prices are cast solely from the mold of financial statements and market trends.
But what tantalizing forces are at play in this hazy waltz of stock prices and pollutants? Is it the interplay of nitrogen oxides and market pressures? Could it be the flirtatious dance of sulfur compounds and investor sentiment? We invite you to join us in peeling back the layers of this atmospheric intrigue, to probe the smoggy enigma that lies beneath the surface of financial indices and market reports.
So, let us embark on this whimsical odyssey and delve into the nebulous alchemy of smogonomics, where air pollution and stock prices twirl together in an unexpected duet of numbers and particulates. Prepare to be enthralled, for we are about to uncover a relationship that's more than just hot air—it's a symphony of eternally intertwined variables that hold the potential to surprise, stupefy, and perhaps even amuse the most discerning of economic intellects.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The examination of the relationship between air pollution and stock prices has long been a subject of interest, offering a unique intersection of environmental science and financial economics. In "The Air We Breathe: Pollution and Financial Markets," Smith and Doe offer an early exploration of this junction, drawing attention to the potential impact of airborne toxins on market fluctuations. Similarly, Jones et al., in "Emissions and Equities: A Smoggy Affair," provide a detailed analysis of air quality indices and their correlation with stock values, laying the groundwork for our current investigation.

Venturing into more unconventional literature, "The Economics of Fog: A Tale of Cloudy Assets" by Greenberg and Reed delves into the metaphysical implications of misty climates on investment decisions, drawing upon ancient folklore and modern-day market psychology to paint an evocative portrait of speculative haze. The whimsical musings of "The Windy Path to Wealth: A Novella of Stock Market Breezes" by Stonebridge take readers on a metaphorical journey through tempestuous stock market seas, offering a fresh perspective on the atmospheric influences that shape financial tides.

For this study, we spared no avenue of inquiry, including thorough examinations of information sources as diverse as the "CVS Receipt Chronicles: Quirky Data Finds in the Aisles" - a compilation of whimsical musings and impromptu poetry found on the back of grocery store receipts. While not traditionally associated with rigorous scientific inquiry, these curiously insightful scribblings unexpectedly shed light on the atmospheric whims that may influence consumer spending and, by extension, stock market dynamics.

As we sift through this diverse tapestry of research, our understanding of the intricate relationship between air pollution and stock prices continues to evolve, leading us to ponder the enigmatic dance of particles and profits. While some may raise an eyebrow at the unorthodox paths we've traversed in pursuit of knowledge, we stand by our unconventional approach as a testament to the boundless creativity and unyielding curiosity that fuels our scholarly expedition.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Phoenix and ORIX Corporation's stock price (IX). Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from 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]]
Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price

[[ABSTRACT]]
In this study, we dive into the atmospheric abyss to examine the uncharted territory of air pollution's influence on stock prices. While most financial analysts focus on traditional market indicators, our research takes a breath of fresh air by exploring the impact of air quality on stock performance. Through rigorous analysis of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we uncovered a surprising connection between the hazy skies of Phoenix and the fluctuating stock price of ORIX Corporation (IX). Our findings reveal a statistically significant correlation coefficient of 0.6449322 and p < 0.01 for the period spanning 2002 to 2023. Prepare to be blown away as we unravel the mysterious interplay between pollution particles and stock market percolations, shedding light on a relationship that's more than just hot air.

[[INTRODUCTION]]
In the grand symphony of economic analyses and market evaluations, one often overlooks the ethereal melodies carried by the winds of air pollution. While conventional economic models may seem as clear as a crisply laundered lab coat, there exists a hazy frontier of financial dynamics that can truly take your breath away. As we wade into the foggy depths of this uncharted territory, we uncover a tale of two spheres colliding in a dance of uncertainty and volatility.
As financial enthusiasts, we are ever in pursuit of understanding the intricate dance of the stock market, diligently sifting through mountains of data like treasure hunters in a sea of numbers. However, in our quest for monetary enlightenment, we sometimes forget to take a deep breath and consider the larger atmospheric forces at play. In this study, we aim to unravel the enigmatic relationship between air pollution in Phoenix and the stock price of ORIX Corporation (IX), combining the esoteric arts of smogonomics with the age-old dance of supply and demand.
While the mere mention of air pollution may conjure images of ominous plumes and disgruntled trees, we bring a fresh perspective to this atmospheric imbroglio. Our journey takes us through the swirling mists of Phoenix—a city known for its blazing sunsets and, more recently, its perplexing influence on financial barometers. Partnering with the Environmental Protection Agency and LSEG Analytics (Refinitiv), we embark on a quest through data thickets and statistical thickets, armed with our trusty compass of hypothesis testing and a keen sense of scientific curiosity.
The serendipitous discoveries that scatter our path are enough to send any statistician's heart aflutter. Brace yourself, for we have unearthed a statistically significant correlation coefficient of 0.6449322, coupled with a p-value that would make even the most stoic of researchers raise an eyebrow—all spanning the eventful period from 2002 to 2023. Much like a chemical reaction in the lab, this empirical linkage defies the conventional wisdom that stock prices are cast solely from the mold of financial statements and market trends.
But what tantalizing forces are at play in this hazy waltz of stock prices and pollutants? Is it the interplay of nitrogen oxides and market pressures? Could it be the flirtatious dance of sulfur compounds and investor sentiment? We invite you to join us in peeling back the layers of this atmospheric intrigue, to probe the smoggy enigma that lies beneath the surface of financial indices and market reports.
So, let us embark on this whimsical odyssey and delve into the nebulous alchemy of smogonomics, where air pollution and stock prices twirl together in an unexpected duet of numbers and particulates. Prepare to be enthralled, for we are about to uncover a relationship that's more than just hot air—it's a symphony of eternally intertwined variables that hold the potential to surprise, stupefy, and perhaps even amuse the most discerning of economic intellects.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To unearth the tantalizing connection between the aerial ballet of air pollution in Phoenix and the graceful movements of ORIX Corporation's stock price (IX), our research team embarked on a spirited quest through the data thickets and statistical thickets, armed with the noble pursuit of scientific scrutiny and a smattering of good-natured humor.

Data Collection:
Leaping into action, we cast a wide net across the internet, harnessing the power of web-scouring prowess to gather a comprehensive dataset from the Environmental Protection Agency and LSEG Analytics (Refinitiv). Armed with data spanning the years 2002 to 2023, we left no pixel unturned in our quest for statistical treasures. Our research team was stationed in a laboratory that was more Wizard of Oz than Frankenstein, combining the art of data collection with a dash of technological magic and an ample serving of perseverance.

Air Pollution Metrics:
As any would-be wizard knows, it takes a concoction of potent ingredients to brew a spellbinding potion. In our case, these ingredients were none other than the atmospheric metrics garnered from the Environmental Protection Agency. We set our sights on the ethereal dance of the airborne contaminants, including particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO). Each of these atmospheric maestros added their own distinctive notes to the symphony of pollution, and we were determined to capture their harmonious yet discordant interactions with precision.

Stock Price Data:
Turning our gaze to the fickle world of stock prices, we tapped into the rich tapestry of market data provided by LSEG Analytics (Refinitiv). The stock price of ORIX Corporation (IX) emerged as our focal point, as we traced its undulating path through the tempestuous seas of market fluctuations. Much like seasoned sailors, we braved the stormy fluctuations and choppy tides of financial data, aiming to uncover the hidden currents that tied the company's stock price to the atmospheric whims of Phoenix.

Statistical Analysis:
With our multidimensional dataset in hand, we harnessed the power of statistical sorcery to unveil the elusive link between air pollution and stock price. Our analytical arsenal included the formidable weaponry of correlation analysis, regression modeling, and hypothesis testing. Like audacious alchemists, we sought to transmute raw data into enlightening insights, and to uncover the scarcely glimpsed correlations amidst the sea of numbers and symbols.

In reviewing our methodology, we recognize that our data collection process might have appeared more whimsical than a physics lesson at Hogwarts, but we assure the scientific community that our efforts were guided by rigor, perseverance, and a zest for uncovering the unconventional interplay between two seemingly disparate phenomena. As we proceed to unveil our findings in the subsequent sections, we invite our peers to set sail with us on this adventurous journey through the hazy mists of smogonomics, where science and whimsy converge in a harmonious cacophony of discovery and amusement.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Phoenix and ORIX Corporation's stock price (IX). Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and LSEG Analytics (Refinitiv) .

For the time period 2002 to 2023, you found a correlation 0.6449322, r-squared of 0.4159376, 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]]
Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price

[[ABSTRACT]]
In this study, we dive into the atmospheric abyss to examine the uncharted territory of air pollution's influence on stock prices. While most financial analysts focus on traditional market indicators, our research takes a breath of fresh air by exploring the impact of air quality on stock performance. Through rigorous analysis of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we uncovered a surprising connection between the hazy skies of Phoenix and the fluctuating stock price of ORIX Corporation (IX). Our findings reveal a statistically significant correlation coefficient of 0.6449322 and p < 0.01 for the period spanning 2002 to 2023. Prepare to be blown away as we unravel the mysterious interplay between pollution particles and stock market percolations, shedding light on a relationship that's more than just hot air.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the grand symphony of economic analyses and market evaluations, one often overlooks the ethereal melodies carried by the winds of air pollution. While conventional economic models may seem as clear as a crisply laundered lab coat, there exists a hazy frontier of financial dynamics that can truly take your breath away. As we wade into the foggy depths of this uncharted territory, we uncover a tale of two spheres colliding in a dance of uncertainty and volatility.
As financial enthusiasts, we are ever in pursuit of understanding the intricate dance of the stock market, diligently sifting through mountains of data like treasure hunters in a sea of numbers. However, in our quest for monetary enlightenment, we sometimes forget to take a deep breath and consider the larger atmospheric forces at play. In this study, we aim to unravel the enigmatic relationship between air pollution in Phoenix and the stock price of ORIX Corporation (IX), combining the esoteric arts of smogonomics with the age-old dance of supply and demand.
While the mere mention of air pollution may conjure images of ominous plumes and disgruntled trees, we bring a fresh perspective to this atmospheric imbroglio. Our journey takes us through the swirling mists of Phoenix—a city known for its blazing sunsets and, more recently, its perplexing influence on financial barometers. Partnering with the Environmental Protection Agency and LSEG Analytics (Refinitiv), we embark on a quest through data thickets and statistical thickets, armed with our trusty compass of hypothesis testing and a keen sense of scientific curiosity.
The serendipitous discoveries that scatter our path are enough to send any statistician's heart aflutter. Brace yourself, for we have unearthed a statistically significant correlation coefficient of 0.6449322, coupled with a p-value that would make even the most stoic of researchers raise an eyebrow—all spanning the eventful period from 2002 to 2023. Much like a chemical reaction in the lab, this empirical linkage defies the conventional wisdom that stock prices are cast solely from the mold of financial statements and market trends.
But what tantalizing forces are at play in this hazy waltz of stock prices and pollutants? Is it the interplay of nitrogen oxides and market pressures? Could it be the flirtatious dance of sulfur compounds and investor sentiment? We invite you to join us in peeling back the layers of this atmospheric intrigue, to probe the smoggy enigma that lies beneath the surface of financial indices and market reports.
So, let us embark on this whimsical odyssey and delve into the nebulous alchemy of smogonomics, where air pollution and stock prices twirl together in an unexpected duet of numbers and particulates. Prepare to be enthralled, for we are about to uncover a relationship that's more than just hot air—it's a symphony of eternally intertwined variables that hold the potential to surprise, stupefy, and perhaps even amuse the most discerning of economic intellects.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

Our study probed the murky depths of air pollution in Phoenix and its intriguing connection to the stock price of ORIX Corporation (IX) from 2002 to 2023. As we unveiled the results of our statistical foray, we were met with a surprising correlation coefficient of 0.6449322 and an r-squared of 0.4159376, casting a shadow of statistical significance over the relationship between these seemingly disparate variables. With a p-value of less than 0.01, our findings left us breathless with excitement, much like an unexpected twist in a suspenseful thriller.

The relationship between air pollution in Phoenix and ORIX Corporation's stock price unfurled like a dramatic tango on the dance floor of statistical intrigue. Fig. 1 aptly captures the dynamic duo in action, a scatterplot revealing a compelling and unmistakable trend that ties the hazy skies of Phoenix to the ebbs and flows of ORIX Corporation's stock price.

In a dance of uncertainty and volatility, our results waltzed to the rhythm of statistical significance, pirouetting around the conventional wisdom that economic forces operate in isolation from atmospheric realms. As we unmasked the veil of mystery enshrouding this unusual partnership, we were reminded that in the world of research, the most captivating revelations often emerge from the unlikeliest of twosomes.

Our findings not only fuel the flames of curiosity but also offer a breath of fresh air in the realm of financial analysis. Let this serve as a clarion call to the economic stalwarts and the adventurous scientists alike: the atmospheric tango of air pollution and stock prices beckons, inviting you to witness its enigmatic performance and partake in the whimsical odyssey of smogonomics.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Phoenix and ORIX Corporation's stock price (IX). Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price

[[ABSTRACT]]
In this study, we dive into the atmospheric abyss to examine the uncharted territory of air pollution's influence on stock prices. While most financial analysts focus on traditional market indicators, our research takes a breath of fresh air by exploring the impact of air quality on stock performance. Through rigorous analysis of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we uncovered a surprising connection between the hazy skies of Phoenix and the fluctuating stock price of ORIX Corporation (IX). Our findings reveal a statistically significant correlation coefficient of 0.6449322 and p < 0.01 for the period spanning 2002 to 2023. Prepare to be blown away as we unravel the mysterious interplay between pollution particles and stock market percolations, shedding light on a relationship that's more than just hot air.

[[LITERATURE REVIEW]]
The examination of the relationship between air pollution and stock prices has long been a subject of interest, offering a unique intersection of environmental science and financial economics. In "The Air We Breathe: Pollution and Financial Markets," Smith and Doe offer an early exploration of this junction, drawing attention to the potential impact of airborne toxins on market fluctuations. Similarly, Jones et al., in "Emissions and Equities: A Smoggy Affair," provide a detailed analysis of air quality indices and their correlation with stock values, laying the groundwork for our current investigation.
Venturing into more unconventional literature, "The Economics of Fog: A Tale of Cloudy Assets" by Greenberg and Reed delves into the metaphysical implications of misty climates on investment decisions, drawing upon ancient folklore and modern-day market psychology to paint an evocative portrait of speculative haze. The whimsical musings of "The Windy Path to Wealth: A Novella of Stock Market Breezes" by Stonebridge take readers on a metaphorical journey through tempestuous stock market seas, offering a fresh perspective on the atmospheric influences that shape financial tides.
For this study, we spared no avenue of inquiry, including thorough examinations of information sources as diverse as the "CVS Receipt Chronicles: Quirky Data Finds in the Aisles" - a compilation of whimsical musings and impromptu poetry found on the back of grocery store receipts. While not traditionally associated with rigorous scientific inquiry, these curiously insightful scribblings unexpectedly shed light on the atmospheric whims that may influence consumer spending and, by extension, stock market dynamics.
As we sift through this diverse tapestry of research, our understanding of the intricate relationship between air pollution and stock prices continues to evolve, leading us to ponder the enigmatic dance of particles and profits. While some may raise an eyebrow at the unorthodox paths we've traversed in pursuit of knowledge, we stand by our unconventional approach as a testament to the boundless creativity and unyielding curiosity that fuels our scholarly expedition.

[[RESULTS]]
Our study probed the murky depths of air pollution in Phoenix and its intriguing connection to the stock price of ORIX Corporation (IX) from 2002 to 2023. As we unveiled the results of our statistical foray, we were met with a surprising correlation coefficient of 0.6449322 and an r-squared of 0.4159376, casting a shadow of statistical significance over the relationship between these seemingly disparate variables. With a p-value of less than 0.01, our findings left us breathless with excitement, much like an unexpected twist in a suspenseful thriller.
The relationship between air pollution in Phoenix and ORIX Corporation's stock price unfurled like a dramatic tango on the dance floor of statistical intrigue. Fig. 1 aptly captures the dynamic duo in action, a scatterplot revealing a compelling and unmistakable trend that ties the hazy skies of Phoenix to the ebbs and flows of ORIX Corporation's stock price.
In a dance of uncertainty and volatility, our results waltzed to the rhythm of statistical significance, pirouetting around the conventional wisdom that economic forces operate in isolation from atmospheric realms. As we unmasked the veil of mystery enshrouding this unusual partnership, we were reminded that in the world of research, the most captivating revelations often emerge from the unlikeliest of twosomes.
Our findings not only fuel the flames of curiosity but also offer a breath of fresh air in the realm of financial analysis. Let this serve as a clarion call to the economic stalwarts and the adventurous scientists alike: the atmospheric tango of air pollution and stock prices beckons, inviting you to witness its enigmatic performance and partake in the whimsical odyssey of smogonomics.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our findings dance a compelling waltz with prior research, echoing the hazy melodies and dubious duets explored by previous scholars. Smith and Doe's "The Air We Breathe: Pollution and Financial Markets" can be likened to the opening act, laying the groundwork for our smoggy symphony. In a similar tune, "Emissions and Equities: A Smoggy Affair" by Jones et al. sets the stage for our study, underscoring the lingering impact of airborne toxins on market dynamics. The whimsical "The Economics of Fog: A Tale of Cloudy Assets" by Greenberg and Reed, though seemingly enigmatic in its approach, resonates with the atmospheric mystery we seek to unravel. Similarly, the metaphorical journey through tempestuous stock market seas painted in "The Windy Path to Wealth: A Novella of Stock Market Breezes" by Stonebridge mirrors our quest to decipher the winds of change in financial tides.

Venturing into less conventional literature, the "CVS Receipt Chronicles: Quirky Data Finds in the Aisles" braces the quirky storm, unexpectedly illuminating the atmospheric whims influencing consumer spending and, by extension, stock market dynamics. The unorthodox path we've traversed aligns with our scholarly endeavor, fueling our unconventional approach and unyielding curiosity.

Our results, akin to a suspenseful thriller, unfurled a dramatic tango between air pollution in Phoenix and ORIX Corporation's stock price, teasing a surprising correlation coefficient that cast a shadow of statistical significance over these apparently disparate variables. With a p-value of less than 0.01, our findings tantalized the senses, much like an unexpected twist in a suspenseful thriller.

As we unmasked the veil of mystery shrouding this unusual partnership, a breath of fresh air emerged in the realm of financial analysis. This study serves to reinforce the captivating presence of atmospheric influence on market fluctuations, amplifying the call to explore the whimsical odyssey of smogonomics. The atmospheric tango of air pollution and stock prices beckons, inviting economic stalwarts and adventurous scientists to witness its enigmatic performance, showcasing once again that the most captivating revelations often emerge from the unlikeliest of twosomes.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Phoenix and ORIX Corporation's stock price (IX). Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price

[[ABSTRACT]]
In this study, we dive into the atmospheric abyss to examine the uncharted territory of air pollution's influence on stock prices. While most financial analysts focus on traditional market indicators, our research takes a breath of fresh air by exploring the impact of air quality on stock performance. Through rigorous analysis of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we uncovered a surprising connection between the hazy skies of Phoenix and the fluctuating stock price of ORIX Corporation (IX). Our findings reveal a statistically significant correlation coefficient of 0.6449322 and p < 0.01 for the period spanning 2002 to 2023. Prepare to be blown away as we unravel the mysterious interplay between pollution particles and stock market percolations, shedding light on a relationship that's more than just hot air.

[[INTRDUCTION]]
In the grand symphony of economic analyses and market evaluations, one often overlooks the ethereal melodies carried by the winds of air pollution. While conventional economic models may seem as clear as a crisply laundered lab coat, there exists a hazy frontier of financial dynamics that can truly take your breath away. As we wade into the foggy depths of this uncharted territory, we uncover a tale of two spheres colliding in a dance of uncertainty and volatility.
As financial enthusiasts, we are ever in pursuit of understanding the intricate dance of the stock market, diligently sifting through mountains of data like treasure hunters in a sea of numbers. However, in our quest for monetary enlightenment, we sometimes forget to take a deep breath and consider the larger atmospheric forces at play. In this study, we aim to unravel the enigmatic relationship between air pollution in Phoenix and the stock price of ORIX Corporation (IX), combining the esoteric arts of smogonomics with the age-old dance of supply and demand.
While the mere mention of air pollution may conjure images of ominous plumes and disgruntled trees, we bring a fresh perspective to this atmospheric imbroglio. Our journey takes us through the swirling mists of Phoenix—a city known for its blazing sunsets and, more recently, its perplexing influence on financial barometers. Partnering with the Environmental Protection Agency and LSEG Analytics (Refinitiv), we embark on a quest through data thickets and statistical thickets, armed with our trusty compass of hypothesis testing and a keen sense of scientific curiosity.
The serendipitous discoveries that scatter our path are enough to send any statistician's heart aflutter. Brace yourself, for we have unearthed a statistically significant correlation coefficient of 0.6449322, coupled with a p-value that would make even the most stoic of researchers raise an eyebrow—all spanning the eventful period from 2002 to 2023. Much like a chemical reaction in the lab, this empirical linkage defies the conventional wisdom that stock prices are cast solely from the mold of financial statements and market trends.
But what tantalizing forces are at play in this hazy waltz of stock prices and pollutants? Is it the interplay of nitrogen oxides and market pressures? Could it be the flirtatious dance of sulfur compounds and investor sentiment? We invite you to join us in peeling back the layers of this atmospheric intrigue, to probe the smoggy enigma that lies beneath the surface of financial indices and market reports.
So, let us embark on this whimsical odyssey and delve into the nebulous alchemy of smogonomics, where air pollution and stock prices twirl together in an unexpected duet of numbers and particulates. Prepare to be enthralled, for we are about to uncover a relationship that's more than just hot air—it's a symphony of eternally intertwined variables that hold the potential to surprise, stupefy, and perhaps even amuse the most discerning of economic intellects.

[[RESULTS]]
Our study probed the murky depths of air pollution in Phoenix and its intriguing connection to the stock price of ORIX Corporation (IX) from 2002 to 2023. As we unveiled the results of our statistical foray, we were met with a surprising correlation coefficient of 0.6449322 and an r-squared of 0.4159376, casting a shadow of statistical significance over the relationship between these seemingly disparate variables. With a p-value of less than 0.01, our findings left us breathless with excitement, much like an unexpected twist in a suspenseful thriller.
The relationship between air pollution in Phoenix and ORIX Corporation's stock price unfurled like a dramatic tango on the dance floor of statistical intrigue. Fig. 1 aptly captures the dynamic duo in action, a scatterplot revealing a compelling and unmistakable trend that ties the hazy skies of Phoenix to the ebbs and flows of ORIX Corporation's stock price.
In a dance of uncertainty and volatility, our results waltzed to the rhythm of statistical significance, pirouetting around the conventional wisdom that economic forces operate in isolation from atmospheric realms. As we unmasked the veil of mystery enshrouding this unusual partnership, we were reminded that in the world of research, the most captivating revelations often emerge from the unlikeliest of twosomes.
Our findings not only fuel the flames of curiosity but also offer a breath of fresh air in the realm of financial analysis. Let this serve as a clarion call to the economic stalwarts and the adventurous scientists alike: the atmospheric tango of air pollution and stock prices beckons, inviting you to witness its enigmatic performance and partake in the whimsical odyssey of smogonomics.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

After diligently untangling the airy waltz of air pollution in Phoenix and ORIX Corporation's stock price, we stand in awe of the captivating tango between these seemingly detached entities. Our findings unveiled a statistically significant correlation coefficient of 0.6449322 and an r-squared of 0.4159376, leaving us breathless with excitement and a touch of smoggy nostalgia.

As we wrap up this whimsical odyssey, we can't resist a pun or two. The stock market and air quality might seem like an odd couple, but they sure know how to dance, much like an unlikely duo at a masked ball. The results may surprise even the most seasoned financial analysts, but in the world of research, it's the unexpected twists that keep us on the edge of our seats.

Now, to address the lingering question of whether more research is needed in this area—well, it's clear that the connection between air pollution and stock prices is more than just hot air. So, let's give these two partners a break and pivot our investigative lenses toward other enthralling mysteries that await our scientific scrutiny. In conclusion, the hazy skies of Phoenix may have revealed their smoggy secrets, but for now, this aerial dance has reached its crescendo, and we bid adieu to the enigmatic performance of smogonomics.


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 Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Atmospheric Economics and Environmental Finance


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 Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Anthropogenic Atmospheric Anomalies Research (SAAAR)


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 Smogonomics: Unveiling the Aerial Dance of Air Pollution in Phoenix and ORIX Corporation's Stock Price with an abstract of In this study, we dive into the atmospheric abyss to examine the uncharted territory of air pollution's influence on stock prices. While most financial analysts focus on traditional market indicators, our research takes a breath of fresh air by exploring the impact of air quality on stock performance. Through rigorous analysis of data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), we uncovered a surprising connection between the hazy skies of Phoenix and the fluctuating stock price of ORIX Corporation (IX). Our findings reveal a statistically significant correlation coefficient of 0.6449322 and p < 0.01 for the period spanning 2002 to 2023. Prepare to be blown away as we unravel the mysterious interplay between pollution particles and stock market percolations, shedding light on a relationship that's more than just hot air.

ChatGPT:

air pollution, stock price, ORIX Corporation, Phoenix, environmental impact, stock market analysis, correlation coefficient, air quality, pollution particles, financial performance, LSEG Analytics, Environmental Protection Agency, stock market relationship

*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 Phoenix
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Phoenix-Mesa-Scottsdale, AZ
Source: Environmental Protection Agency
See what else correlates with Air pollution in Phoenix

ORIX Corporation's stock price (IX)
Detailed data title: Opening price of ORIX Corporation (IX) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: Via Microsoft Excel Stockhistory function

See what else correlates with ORIX Corporation's stock price (IX)

Correlation r = 0.6449322 (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 = 0.4159376 (Coefficient of determination)
This means 41.6% of the change in the one variable (i.e., ORIX Corporation's stock price (IX)) is predictable based on the change in the other (i.e., Air pollution in Phoenix) over the 22 years from 2002 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0012. 0.0011927748795545158000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.64 in 0.12% of random cases. Said differently, if you correlated 838 random variables Which I absolutely did.
with the same 21 degrees of freedom, 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.

[ 0.31, 0.84 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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


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

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
2002200320042005200620072008200920102011201220132014201520162017201820192020202120222023
Air pollution in Phoenix (Bad air quality days)24.931523.287715.027324.931573.150760.821954.91821.369916.164436.438430.327928.767132.602719.178120.491826.027423.287714.794562.841565.205529.041121.1679
ORIX Corporation's stock price (IX) (Stock price)44.3932.240.8568.112814785.1628.5734.3749.0541.9157.788.9263.4269.1978.685.0171.0884.0876.77102.0381.28




Why this works

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




Try it yourself

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

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

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

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

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

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

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

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

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


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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([24.9315,23.2877,15.0273,24.9315,73.1507,60.8219,54.918,21.3699,16.1644,36.4384,30.3279,28.7671,32.6027,19.1781,20.4918,26.0274,23.2877,14.7945,62.8415,65.2055,29.0411,21.1679,])
array_2 = np.array([44.39,32.2,40.85,68.1,128,147,85.16,28.57,34.37,49.05,41.91,57.7,88.92,63.42,69.19,78.6,85.01,71.08,84.08,76.77,102.03,81.28,])
array_1_name = "Air pollution in Phoenix"
array_2_name = "ORIX Corporation's stock price (IX)"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)



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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."

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Correlation ID: 3996 · Black Variable ID: 21061 · Red Variable ID: 1887
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