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Spurious correlation #3,840 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of garbage collectors in Texas and the second variable is MetLife's stock price (MET).  The chart goes from 2003 to 2022, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As the garbage collectors in Texas multiplied, so did their efficiency in picking up the trash. This inadvertently led to a significant reduction in MetLife's risk exposure, prompting investors to 'waste' no time in snatching up their stocks. After all, a cleaner state means a 'tidier' financial outlook for the insurance company, and the market simply couldn't 'bin' ignoring this correlation. So, as the saying goes, one person's trash is another person's ticket to a bullish market!

Model: dalle-3
Prompt: Generate an image of a bustling Texas town with multiple garbage collectors working, leading to an increase in litter being collected. Show overflowing bin bags being discarded by residents, indicating the rising amount of trash. In the background, depict the city's growth with buildings and infrastructure, and a visual representation of MetLife's stock price (MET) soaring to reflect the surge in insurance coverage demand. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.
This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: The number of garbage collectors in Texas and MetLife's stock price (MET). 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 graphics Low poly A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Realistic Cybernetic art
ChatGPT: Realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of garbage collectors in Texas and MetLife's stock price (MET), in the following situation: As the number of garbage collectors in Texas increased, so did the amount of garbage they collected, leading to more trash being thrown out, which inadvertently boosted the demand for insurance coverage, causing MetLife's stock price to rise.. Keep the description simple.
ChatGPT: In a bustling Texas town, the number of garbage collectors multiplied, leading to more litter being collected. As the amount of trash increased, more bin bags overflowed and were thrown out by residents. Meanwhile, amidst the city's growth, the demand for insurance coverage surged, causing MetLife's stock price (MET) to soar.

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: [ Realistic ] and depicting this scene: In a bustling Texas town, the number of garbage collectors multiplied, leading to more litter being collected. As the amount of trash increased, more bin bags overflowed and were thrown out by residents. Meanwhile, amidst the city's growth, the demand for insurance coverage surged, causing MetLife's stock price (MET) to soar. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a bustling Texas town with multiple garbage collectors working, leading to an increase in litter being collected. Show overflowing bin bags being discarded by residents, indicating the rising amount of trash. In the background, depict the city's growth with buildings and infrastructure, and a visual representation of MetLife's stock price (MET) soaring to reflect the surge in insurance coverage demand.

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

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

Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in The number of garbage collectors in Texas caused MetLife's stock price (MET) to increase. Include one good pun.

AI academic paper

(Because p < 0.01)
Trash Dash: The Flash Clash with MetLife's Cash Stash

The Journal of Absurd Finance and Nonsense Economics

Jan 2024

Reminder: This paper is AI-generated. Not real!
Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.




Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between The number of garbage collectors in Texas and MetLife's stock price (MET). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Rhyme words in the title.

Your research team used data from Bureau of Larbor Statistics and LSEG Analytics (Refinitiv) to assess this nagging question. You found a correlation coefficient of 0.8332878 and p < 0.01 for 2003 to 2022.

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]]
Trash Dash: The Flash Clash with MetLife's Cash Stash

[[ABSTRACT]]
This study examined the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team identified a remarkably high correlation coefficient of 0.8332878 and a statistically significant p-value of less than 0.01, suggesting a strong connection between these seemingly unrelated variables. While our findings do not imply causation, they do raise intriguing questions about the underpinnings of market behavior and the possibility of "trash talk" influencing stock performance. Our results offer a fresh perspective on the intricate interplay between waste management and financial markets, demonstrating that one person's trash may indeed be another person's (or corporation's) treasure.


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

Please make up the introduction section of an academic research paper presenting the findings of the connection between The number of garbage collectors in Texas and MetLife's stock price (MET). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
Trash Dash: The Flash Clash with MetLife's Cash Stash

[[ABSTRACT]]
This study examined the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team identified a remarkably high correlation coefficient of 0.8332878 and a statistically significant p-value of less than 0.01, suggesting a strong connection between these seemingly unrelated variables. While our findings do not imply causation, they do raise intriguing questions about the underpinnings of market behavior and the possibility of "trash talk" influencing stock performance. Our results offer a fresh perspective on the intricate interplay between waste management and financial markets, demonstrating that one person's trash may indeed be another person's (or corporation's) treasure.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

The relationship between seemingly unrelated variables has long piqued the curiosity of researchers and market enthusiasts alike. Whether it's the classic debate of the chicken and the egg, the perplexing correlation between the consumption of mozzarella cheese and civil engineering doctorates awarded, or the intriguing connection between the number of garbage collectors in Texas and the stock price of MetLife (MET), the world of statistical analysis continues to uncover surprising and, at times, astonishing relationships.

This study delves into the peculiar association between the labor force responsible for waste management in the Lone Star State and the fluctuations in MetLife's stock price over the period spanning from 2003 to 2022. While on the surface, "trash" and "cash" may seem as disparate as chalk and cheese, our investigation sheds light on the considerable correlation between these variables.

Through the utilization of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team has meticulously combed through the numbers to reveal a strikingly high correlation coefficient of 0.8332878. This research endeavor has led us to conclude that, indeed, there appears to be a surprising synchronicity between the labor force engaged in waste management and the financial performance of MetLife.

As we embark on this analytical journey, it is imperative to acknowledge that correlation does not imply causation. However, the statistical significance of our findings, with a p-value of less than 0.01, accentuates the strength of the association we have unearthed. The confluence of rubbish collection and stock prices has ushered in a new perspective on the intricate dynamics of market behavior and the essence of market psychology.

The unexpectedly robust relationship identified in this study prompts contemplation on the potential impact of what could euphemistically be dubbed "trash talk" on financial markets. This raises a new avenue of inquiry into the hitherto unexplored influence of waste management on stock performance.

In the spirit of scientific inquiry, we invite the reader to journey with us through the labyrinth of statistical analysis, and to contemplate the adage that "one person's trash may indeed be another person's (or corporation's) treasure."


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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of garbage collectors in Texas and MetLife's stock price (MET). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
Trash Dash: The Flash Clash with MetLife's Cash Stash

[[ABSTRACT]]
This study examined the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team identified a remarkably high correlation coefficient of 0.8332878 and a statistically significant p-value of less than 0.01, suggesting a strong connection between these seemingly unrelated variables. While our findings do not imply causation, they do raise intriguing questions about the underpinnings of market behavior and the possibility of "trash talk" influencing stock performance. Our results offer a fresh perspective on the intricate interplay between waste management and financial markets, demonstrating that one person's trash may indeed be another person's (or corporation's) treasure.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The relationship between seemingly unrelated variables has long piqued the curiosity of researchers and market enthusiasts alike. Whether it's the classic debate of the chicken and the egg, the perplexing correlation between the consumption of mozzarella cheese and civil engineering doctorates awarded, or the intriguing connection between the number of garbage collectors in Texas and the stock price of MetLife (MET), the world of statistical analysis continues to uncover surprising and, at times, astonishing relationships.
This study delves into the peculiar association between the labor force responsible for waste management in the Lone Star State and the fluctuations in MetLife's stock price over the period spanning from 2003 to 2022. While on the surface, "trash" and "cash" may seem as disparate as chalk and cheese, our investigation sheds light on the considerable correlation between these variables.
Through the utilization of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team has meticulously combed through the numbers to reveal a strikingly high correlation coefficient of 0.8332878. This research endeavor has led us to conclude that, indeed, there appears to be a surprising synchronicity between the labor force engaged in waste management and the financial performance of MetLife.
As we embark on this analytical journey, it is imperative to acknowledge that correlation does not imply causation. However, the statistical significance of our findings, with a p-value of less than 0.01, accentuates the strength of the association we have unearthed. The confluence of rubbish collection and stock prices has ushered in a new perspective on the intricate dynamics of market behavior and the essence of market psychology.
The unexpectedly robust relationship identified in this study prompts contemplation on the potential impact of what could euphemistically be dubbed "trash talk" on financial markets. This raises a new avenue of inquiry into the hitherto unexplored influence of waste management on stock performance.
In the spirit of scientific inquiry, we invite the reader to journey with us through the labyrinth of statistical analysis, and to contemplate the adage that "one person's trash may indeed be another person's (or corporation's) treasure."

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The curious correlation between the number of garbage collectors in Texas and the stock price of MetLife (MET) has sparked numerous studies exploring the unexpected rapport between waste management and financial markets. Smith (2010) conducted a comprehensive analysis of waste management labor forces across various states and their potential influence on stock performance, laying the groundwork for subsequent research. Doe (2014) delved into the psychological underpinnings of market behavior, drawing intriguing parallels between the disposal of refuse and investor sentiment. Jones (2018) ventured into the realm of environmental economics, examining the impact of waste management practices on corporate valuations, albeit without explicitly focusing on a specific company such as MetLife.

In "Rubbish Realities: The Untold Story of Waste Management" (Green, 2015), the author divulges the clandestine world of garbage collection, shedding light on the intricate complexities of waste disposal and its potential implications on the financial sphere. Additionally, "The Trash Trove: A Tale of Hidden Treasures" (Silver, 2012) presents a fictional account of a garbage collector stumbling upon a remarkable discovery, serving as a whimsical metaphor for the unexplored value hidden within waste.

Moreover, the animated series "Trash Tales" and the children's show "Trash Treasures" provided insightful perspectives on the cultural significance of waste, albeit from a more lighthearted and whimsical vantage point. These unconventional sources stimulated contemplation on the multifaceted dimensions of waste and its interaction with broader societal and economic phenomena, albeit in a non-traditional manner.

As we navigate the expanse of existing literature, it becomes evident that the connection between waste management and stock prices transcends conventional boundaries and evokes thought-provoking inquiries into the intricate interplay between seemingly incongruous domains.


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

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of garbage collectors in Texas and MetLife's stock price (MET). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Bureau of Larbor Statistics and LSEG Analytics (Refinitiv) . You used data from 2003 to 2022

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]]
Trash Dash: The Flash Clash with MetLife's Cash Stash

[[ABSTRACT]]
This study examined the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team identified a remarkably high correlation coefficient of 0.8332878 and a statistically significant p-value of less than 0.01, suggesting a strong connection between these seemingly unrelated variables. While our findings do not imply causation, they do raise intriguing questions about the underpinnings of market behavior and the possibility of "trash talk" influencing stock performance. Our results offer a fresh perspective on the intricate interplay between waste management and financial markets, demonstrating that one person's trash may indeed be another person's (or corporation's) treasure.

[[INTRODUCTION]]
The relationship between seemingly unrelated variables has long piqued the curiosity of researchers and market enthusiasts alike. Whether it's the classic debate of the chicken and the egg, the perplexing correlation between the consumption of mozzarella cheese and civil engineering doctorates awarded, or the intriguing connection between the number of garbage collectors in Texas and the stock price of MetLife (MET), the world of statistical analysis continues to uncover surprising and, at times, astonishing relationships.
This study delves into the peculiar association between the labor force responsible for waste management in the Lone Star State and the fluctuations in MetLife's stock price over the period spanning from 2003 to 2022. While on the surface, "trash" and "cash" may seem as disparate as chalk and cheese, our investigation sheds light on the considerable correlation between these variables.
Through the utilization of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team has meticulously combed through the numbers to reveal a strikingly high correlation coefficient of 0.8332878. This research endeavor has led us to conclude that, indeed, there appears to be a surprising synchronicity between the labor force engaged in waste management and the financial performance of MetLife.
As we embark on this analytical journey, it is imperative to acknowledge that correlation does not imply causation. However, the statistical significance of our findings, with a p-value of less than 0.01, accentuates the strength of the association we have unearthed. The confluence of rubbish collection and stock prices has ushered in a new perspective on the intricate dynamics of market behavior and the essence of market psychology.
The unexpectedly robust relationship identified in this study prompts contemplation on the potential impact of what could euphemistically be dubbed "trash talk" on financial markets. This raises a new avenue of inquiry into the hitherto unexplored influence of waste management on stock performance.
In the spirit of scientific inquiry, we invite the reader to journey with us through the labyrinth of statistical analysis, and to contemplate the adage that "one person's trash may indeed be another person's (or corporation's) treasure."

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

This study employed a multifaceted approach to investigate the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. The data collection process involved thorough mining of information from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), akin to scavenging for valuable nuggets amidst a sea of statistical debris.

To quantify the magnitude of the relationship, a series of statistical analyses were conducted, resembling the careful sifting through garbage to extract the hidden gems within. The first step involved calculating the correlation coefficient between the number of garbage collectors in Texas and MetLife's stock price, utilizing various mathematical tools to discern patterns amidst the numerical wilderness.

Additionally, we harnessed the power of regression analysis to ascertain the strength and direction of the relationship between the two variables, employing the statistical equivalent of a magnifying glass to scrutinize the intricate details of the data. This process involved fitting a regression model to the data, akin to assembling a complex puzzle where each piece represented a unique aspect of the relationship under investigation.

Furthermore, in order to evaluate the statistical significance of the identified relationship, hypothesis testing was performed with the precision of a skilled archer aiming for the bullseye. The p-value derived from these tests served as a critical gauge of the relationship's robustness, serving as a litmus test for the authenticity of our findings.

Moreover, to ensure the validity and reliability of our results, a meticulous series of sensitivity analyses and robustness checks were conducted, akin to subjecting our findings to a battery of stringent examinations to withstand the rigors of academic scrutiny.

Finally, a comprehensive time-series analysis was executed to discern any temporal patterns or trends in the relationship, resembling the unraveling of a captivating mystery as we navigated through the chronological ebbs and flows of the data.

In summary, the methodology employed in this study reflects a rigorous and comprehensive effort to dissect the enigmatic relationship between the number of garbage collectors in Texas and MetLife's stock price, akin to the meticulous dissection of a perplexing riddle to reveal its hidden secrets.


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

Please make up the results section of an academic research paper presenting the findings of the connection between The number of garbage collectors in Texas and MetLife's stock price (MET). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Bureau of Larbor Statistics and LSEG Analytics (Refinitiv) .

For the time period 2003 to 2022, you found a correlation 0.8332878, r-squared of 0.6943685, 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]]
Trash Dash: The Flash Clash with MetLife's Cash Stash

[[ABSTRACT]]
This study examined the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team identified a remarkably high correlation coefficient of 0.8332878 and a statistically significant p-value of less than 0.01, suggesting a strong connection between these seemingly unrelated variables. While our findings do not imply causation, they do raise intriguing questions about the underpinnings of market behavior and the possibility of "trash talk" influencing stock performance. Our results offer a fresh perspective on the intricate interplay between waste management and financial markets, demonstrating that one person's trash may indeed be another person's (or corporation's) treasure.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The relationship between seemingly unrelated variables has long piqued the curiosity of researchers and market enthusiasts alike. Whether it's the classic debate of the chicken and the egg, the perplexing correlation between the consumption of mozzarella cheese and civil engineering doctorates awarded, or the intriguing connection between the number of garbage collectors in Texas and the stock price of MetLife (MET), the world of statistical analysis continues to uncover surprising and, at times, astonishing relationships.
This study delves into the peculiar association between the labor force responsible for waste management in the Lone Star State and the fluctuations in MetLife's stock price over the period spanning from 2003 to 2022. While on the surface, "trash" and "cash" may seem as disparate as chalk and cheese, our investigation sheds light on the considerable correlation between these variables.
Through the utilization of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team has meticulously combed through the numbers to reveal a strikingly high correlation coefficient of 0.8332878. This research endeavor has led us to conclude that, indeed, there appears to be a surprising synchronicity between the labor force engaged in waste management and the financial performance of MetLife.
As we embark on this analytical journey, it is imperative to acknowledge that correlation does not imply causation. However, the statistical significance of our findings, with a p-value of less than 0.01, accentuates the strength of the association we have unearthed. The confluence of rubbish collection and stock prices has ushered in a new perspective on the intricate dynamics of market behavior and the essence of market psychology.
The unexpectedly robust relationship identified in this study prompts contemplation on the potential impact of what could euphemistically be dubbed "trash talk" on financial markets. This raises a new avenue of inquiry into the hitherto unexplored influence of waste management on stock performance.
In the spirit of scientific inquiry, we invite the reader to journey with us through the labyrinth of statistical analysis, and to contemplate the adage that "one person's trash may indeed be another person's (or corporation's) treasure."

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

In this section, we present the results of our investigation into the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. The seemingly incongruous connection between waste management and financial markets has yielded some unexpected and thought-provoking findings.

Our analysis revealed a remarkably high correlation coefficient of 0.8332878 between the number of garbage collectors in Texas and MetLife's stock price, indicating a strong positive relationship between these variables. This robust correlation coefficient suggests that as the number of garbage collectors in Texas fluctuated, so too did MetLife's stock price, lending credence to the notion that "trash" and "cash" may indeed be more intertwined than previously thought. It appears that the old adage "one person's trash is another person's treasure" may hold true in the realm of market dynamics, albeit in an unconventional and somewhat trashy manner.

Furthermore, the calculated r-squared value of 0.6943685 indicates that approximately 69.44% of the variability in MetLife's stock price can be explained by the fluctuations in the number of garbage collectors in Texas. This substantial proportion of explained variance underscores the substantial influence of waste management labor force on the financial performance of MetLife. It seems that the hidden, nigh invisible hand of garbage collectors may have wields a surprisingly heavy influence on the stock market, raising questions about the previously overlooked impact of waste management on market dynamics.

Additionally, the p-value of less than 0.01 lends strong statistical significance to our findings, further reinforcing the strength of the association between these seemingly disparate variables. This statistical significance underscores the peculiarity and strength of the discovered relationship, compelling us to seriously consider the implications of "trash talk" on stock performance. It seems that the influence of waste management on market behavior, while perhaps initially discarded as inconsequential, holds a weighty and somewhat trashy significance - a seemingly discredited factor with real market impact.

Given the compelling nature of these results, it may be high time for the financial world to take out the trash and reassess the underappreciated significance of waste management labor force on market dynamics. These findings offer a fresh perspective on the intricate interplay between seemingly unrelated factors, and prompt further investigation and contemplation into the unexpected connection between trash and MetLife's cash.


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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of garbage collectors in Texas and MetLife's stock price (MET). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
Trash Dash: The Flash Clash with MetLife's Cash Stash

[[ABSTRACT]]
This study examined the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team identified a remarkably high correlation coefficient of 0.8332878 and a statistically significant p-value of less than 0.01, suggesting a strong connection between these seemingly unrelated variables. While our findings do not imply causation, they do raise intriguing questions about the underpinnings of market behavior and the possibility of "trash talk" influencing stock performance. Our results offer a fresh perspective on the intricate interplay between waste management and financial markets, demonstrating that one person's trash may indeed be another person's (or corporation's) treasure.

[[LITERATURE REVIEW]]
The curious correlation between the number of garbage collectors in Texas and the stock price of MetLife (MET) has sparked numerous studies exploring the unexpected rapport between waste management and financial markets. Smith (2010) conducted a comprehensive analysis of waste management labor forces across various states and their potential influence on stock performance, laying the groundwork for subsequent research. Doe (2014) delved into the psychological underpinnings of market behavior, drawing intriguing parallels between the disposal of refuse and investor sentiment. Jones (2018) ventured into the realm of environmental economics, examining the impact of waste management practices on corporate valuations, albeit without explicitly focusing on a specific company such as MetLife.
In "Rubbish Realities: The Untold Story of Waste Management" (Green, 2015), the author divulges the clandestine world of garbage collection, shedding light on the intricate complexities of waste disposal and its potential implications on the financial sphere. Additionally, "The Trash Trove: A Tale of Hidden Treasures" (Silver, 2012) presents a fictional account of a garbage collector stumbling upon a remarkable discovery, serving as a whimsical metaphor for the unexplored value hidden within waste.
Moreover, the animated series "Trash Tales" and the children's show "Trash Treasures" provided insightful perspectives on the cultural significance of waste, albeit from a more lighthearted and whimsical vantage point. These unconventional sources stimulated contemplation on the multifaceted dimensions of waste and its interaction with broader societal and economic phenomena, albeit in a non-traditional manner.
As we navigate the expanse of existing literature, it becomes evident that the connection between waste management and stock prices transcends conventional boundaries and evokes thought-provoking inquiries into the intricate interplay between seemingly incongruous domains.

[[RESULTS]]
In this section, we present the results of our investigation into the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. The seemingly incongruous connection between waste management and financial markets has yielded some unexpected and thought-provoking findings.
Our analysis revealed a remarkably high correlation coefficient of 0.8332878 between the number of garbage collectors in Texas and MetLife's stock price, indicating a strong positive relationship between these variables. This robust correlation coefficient suggests that as the number of garbage collectors in Texas fluctuated, so too did MetLife's stock price, lending credence to the notion that "trash" and "cash" may indeed be more intertwined than previously thought. It appears that the old adage "one person's trash is another person's treasure" may hold true in the realm of market dynamics, albeit in an unconventional and somewhat trashy manner.
Furthermore, the calculated r-squared value of 0.6943685 indicates that approximately 69.44% of the variability in MetLife's stock price can be explained by the fluctuations in the number of garbage collectors in Texas. This substantial proportion of explained variance underscores the substantial influence of waste management labor force on the financial performance of MetLife. It seems that the hidden, nigh invisible hand of garbage collectors may have wields a surprisingly heavy influence on the stock market, raising questions about the previously overlooked impact of waste management on market dynamics.
Additionally, the p-value of less than 0.01 lends strong statistical significance to our findings, further reinforcing the strength of the association between these seemingly disparate variables. This statistical significance underscores the peculiarity and strength of the discovered relationship, compelling us to seriously consider the implications of "trash talk" on stock performance. It seems that the influence of waste management on market behavior, while perhaps initially discarded as inconsequential, holds a weighty and somewhat trashy significance - a seemingly discredited factor with real market impact.
Given the compelling nature of these results, it may be high time for the financial world to take out the trash and reassess the underappreciated significance of waste management labor force on market dynamics. These findings offer a fresh perspective on the intricate interplay between seemingly unrelated factors, and prompt further investigation and contemplation into the unexpected connection between trash and MetLife's cash.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The findings of our study not only corroborate prior research but also provide a deeper understanding of the uncharted territory where waste management and financial markets intersect. The remarkably high correlation coefficient and statistically significant p-value underscore the surprisingly robust connection between the number of garbage collectors in Texas and MetLife's stock price.

Drawing from the literature review, which whimsically explored the cultural significance of waste in "Trash Tales" and "Trash Treasures," our results shed light on the substantial impact of waste management labor force on the financial performance of MetLife. It seems that the clandestine world of garbage collection, as divulged in "Rubbish Realities," possesses an underappreciated yet weighty influence on market dynamics. The hidden treasures metaphorically depicted in "The Trash Trove" take on a new dimension, as the value hidden within waste appears to exert a tangible influence on corporate valuations.

Our findings also resonate with Doe's (2014) examination of investor sentiment and waste disposal, as they suggest that the fluctuations in the number of garbage collectors may indeed reflect shifts in market behavior – a trashy assessment that nevertheless offers actionable insights into financial markets.

The substantial proportion of explained variance in MetLife's stock price, as evidenced by the calculated r-squared value, further reinforces the tangible influence of waste management labor force on market dynamics. It appears that the invisible hand of garbage collectors may not be as invisible as previously assumed, but rather wields a tangible impact on stock performance.

The statistically significant p-value reinforces the idea that "trash talk" may have a real and weighty implication on stock performance. The unexpectedly weighty significance of this previously underestimated factor prompts a reevaluation of the conventional wisdom around market forces, compelling us to seriously consider the trashy influence of waste management on financial markets.

In conclusion, our findings offer a fresh perspective on the intricate interplay between waste management and financial markets, upending the conventional notion of these seemingly unrelated factors. It is of paramount importance for scholars and practitioners alike to recognize and further investigate the undeniable connection between trash and MetLife's cash, challenging us to contemplate the garbage collectors' trashy impact on stock market dynamics.


Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of garbage collectors in Texas and MetLife's stock price (MET). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
Trash Dash: The Flash Clash with MetLife's Cash Stash

[[ABSTRACT]]
This study examined the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team identified a remarkably high correlation coefficient of 0.8332878 and a statistically significant p-value of less than 0.01, suggesting a strong connection between these seemingly unrelated variables. While our findings do not imply causation, they do raise intriguing questions about the underpinnings of market behavior and the possibility of "trash talk" influencing stock performance. Our results offer a fresh perspective on the intricate interplay between waste management and financial markets, demonstrating that one person's trash may indeed be another person's (or corporation's) treasure.

[[INTRDUCTION]]
The relationship between seemingly unrelated variables has long piqued the curiosity of researchers and market enthusiasts alike. Whether it's the classic debate of the chicken and the egg, the perplexing correlation between the consumption of mozzarella cheese and civil engineering doctorates awarded, or the intriguing connection between the number of garbage collectors in Texas and the stock price of MetLife (MET), the world of statistical analysis continues to uncover surprising and, at times, astonishing relationships.
This study delves into the peculiar association between the labor force responsible for waste management in the Lone Star State and the fluctuations in MetLife's stock price over the period spanning from 2003 to 2022. While on the surface, "trash" and "cash" may seem as disparate as chalk and cheese, our investigation sheds light on the considerable correlation between these variables.
Through the utilization of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team has meticulously combed through the numbers to reveal a strikingly high correlation coefficient of 0.8332878. This research endeavor has led us to conclude that, indeed, there appears to be a surprising synchronicity between the labor force engaged in waste management and the financial performance of MetLife.
As we embark on this analytical journey, it is imperative to acknowledge that correlation does not imply causation. However, the statistical significance of our findings, with a p-value of less than 0.01, accentuates the strength of the association we have unearthed. The confluence of rubbish collection and stock prices has ushered in a new perspective on the intricate dynamics of market behavior and the essence of market psychology.
The unexpectedly robust relationship identified in this study prompts contemplation on the potential impact of what could euphemistically be dubbed "trash talk" on financial markets. This raises a new avenue of inquiry into the hitherto unexplored influence of waste management on stock performance.
In the spirit of scientific inquiry, we invite the reader to journey with us through the labyrinth of statistical analysis, and to contemplate the adage that "one person's trash may indeed be another person's (or corporation's) treasure."

[[RESULTS]]
In this section, we present the results of our investigation into the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. The seemingly incongruous connection between waste management and financial markets has yielded some unexpected and thought-provoking findings.
Our analysis revealed a remarkably high correlation coefficient of 0.8332878 between the number of garbage collectors in Texas and MetLife's stock price, indicating a strong positive relationship between these variables. This robust correlation coefficient suggests that as the number of garbage collectors in Texas fluctuated, so too did MetLife's stock price, lending credence to the notion that "trash" and "cash" may indeed be more intertwined than previously thought. It appears that the old adage "one person's trash is another person's treasure" may hold true in the realm of market dynamics, albeit in an unconventional and somewhat trashy manner.
Furthermore, the calculated r-squared value of 0.6943685 indicates that approximately 69.44% of the variability in MetLife's stock price can be explained by the fluctuations in the number of garbage collectors in Texas. This substantial proportion of explained variance underscores the substantial influence of waste management labor force on the financial performance of MetLife. It seems that the hidden, nigh invisible hand of garbage collectors may have wields a surprisingly heavy influence on the stock market, raising questions about the previously overlooked impact of waste management on market dynamics.
Additionally, the p-value of less than 0.01 lends strong statistical significance to our findings, further reinforcing the strength of the association between these seemingly disparate variables. This statistical significance underscores the peculiarity and strength of the discovered relationship, compelling us to seriously consider the implications of "trash talk" on stock performance. It seems that the influence of waste management on market behavior, while perhaps initially discarded as inconsequential, holds a weighty and somewhat trashy significance - a seemingly discredited factor with real market impact.
Given the compelling nature of these results, it may be high time for the financial world to take out the trash and reassess the underappreciated significance of waste management labor force on market dynamics. These findings offer a fresh perspective on the intricate interplay between seemingly unrelated factors, and prompt further investigation and contemplation into the unexpected connection between trash and MetLife's cash.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, the results of this study have brought to light a surprising and compelling connection between the number of garbage collectors in Texas and the stock price of MetLife (MET). The remarkably high correlation coefficient and statistically significant p-value emphasize the strong association between these seemingly unrelated variables. This unearths a new dimension to the intricate tapestry of market dynamics, suggesting that the influence of waste management may not be "garbage" after all.

The substantial proportion of explained variance underscores the weighty impact of waste management labor force on the financial performance of MetLife, demonstrating that the flotsam and jetsam of statistical anomalies may harbor hidden treasures of insight. The significance of "trash talk" in financial markets, while initially discarded as inconsequential, warrants serious consideration and reflection.

While these findings may seem like a "load of rubbish" to some, they undeniably prompt contemplation on the underappreciated influence of waste management on market behavior. Our research adds a fresh perspective to the burgeoning field of offbeat correlations and highlights the need for further examination into the unexpected connection between the mundane world of waste management and the realm of market finance. It appears that, in the grand scheme of statistical relationships, "one person's trash" may indeed hold the key to unlocking new understandings in the world of finance.

In light of these findings, it seems that no more research is needed in this area. After all, as the saying goes, "there's no need to dig through the garbage once the treasure has been found."


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 Trash Dash: The Flash Clash with MetLife's Cash Stash. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Absurd Finance and Nonsense Economics


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 Trash Dash: The Flash Clash with MetLife's Cash Stash. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Absurd Research Endeavors and Nonsensical Studies


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 Trash Dash: The Flash Clash with MetLife's Cash Stash with an abstract of This study examined the relationship between the number of garbage collectors in Texas and MetLife's stock price (MET) from 2003 to 2022. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team identified a remarkably high correlation coefficient of 0.8332878 and a statistically significant p-value of less than 0.01, suggesting a strong connection between these seemingly unrelated variables. While our findings do not imply causation, they do raise intriguing questions about the underpinnings of market behavior and the possibility of "trash talk" influencing stock performance. Our results offer a fresh perspective on the intricate interplay between waste management and financial markets, demonstrating that one person's trash may indeed be another person's (or corporation's) treasure.

ChatGPT:

garbage collectors, trash collection, MetLife stock price, MET stock, Texas garbage collectors, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, trash talk, waste management, stock performance, market behavior

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



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Data details

The number of garbage collectors in Texas
Detailed data title: BLS estimate of refuse and recyclable material collectors in Texas
Source: Bureau of Larbor Statistics
See what else correlates with The number of garbage collectors in Texas

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

See what else correlates with MetLife's stock price (MET)

Correlation r = 0.8332878 (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 = 0.6943685 (Coefficient of determination)
This means 69.4% of the change in the one variable (i.e., MetLife's stock price (MET)) is predictable based on the change in the other (i.e., The number of garbage collectors in Texas) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.07E-6. 0.0000050714942369351960000000
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 0.83 in 0.000507% of random cases. Said differently, if you correlated 197,181 random variables You don't actually need 197 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 19 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 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.

[ 0.62, 0.93 ] 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.
20032004200520062007200820092010201120122013201420152016201720182019202020212022
The number of garbage collectors in Texas (Laborers)5230550061807360847090608300717057005130635073207780776084409050883010060912011830
MetLife's stock price (MET) (Stock price)24.4330.0336.1543.8453.1354.5831.4331.9740.0628.5830.3347.7248.5541.9948.5850.8140.6151.2847.0362.94




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([5230,5500,6180,7360,8470,9060,8300,7170,5700,5130,6350,7320,7780,7760,8440,9050,8830,10060,9120,11830,])
array_2 = np.array([24.43,30.03,36.15,43.84,53.13,54.58,31.43,31.97,40.06,28.58,30.33,47.72,48.55,41.99,48.58,50.81,40.61,51.28,47.03,62.94,])
array_1_name = "The number of garbage collectors in Texas"
array_2_name = "MetLife's stock price (MET)"

# 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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Correlation ID: 3840 · Black Variable ID: 14962 · Red Variable ID: 1733
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