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Spurious correlation #3,266 · 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 truck drivers in Delaware and the second variable is Morgan Stanley's stock price (MS).  The chart goes from 2010 to 2022, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

The increase in truck drivers in Delaware improved the efficiency of delivering goods, leading to a boost in the overall economy. This economic growth resulted in higher investment activity, including in financial services like those offered by Morgan Stanley. As a result, the demand for Morgan Stanley's stock increased, causing the stock price to rise.

Model: dalle-3
Prompt: Style: Impressionist painting Description: In a picturesque Delaware diner, truck drivers are enjoying stacks of mouthwatering pancakes, creating a bustling and lively atmosphere. Bright colors and dynamic brushstrokes capture the energy of the scene, with syrup drizzling down the pancakes. Meanwhile, a TV screen in the background displays the soaring stock price of Morgan Stanley (MS), symbolizing the surge in syrupy asset investments. The painting evokes a sense of warmth, camaraderie, and the excitement of financial success. 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 truck drivers in Delaware and Morgan Stanley's stock price (MS). 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: An image that looks like it is a scene from 'Star Wars' Scalar art Woodblock painting An image that looks like it is a scene from a Pixar movie Brutalism
ChatGPT: Scalar art

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 truck drivers in Delaware and Morgan Stanley's stock price (MS), in the following situation: The increase in truck drivers in Delaware caused a spike in demand for roadside diners, leading to a surge in pancake sales. Morgan Stanley's stock price soared as investors realized that the company holds a monopoly on syrupy assets.. Keep the description simple.
ChatGPT: In a bustling Delaware diner, numerous truck drivers are seen enjoying stacks of freshly made pancakes, creating a busy and lively atmosphere. Meanwhile, on a nearby TV screen, Morgan Stanley's stock price (MS) is shown to be soaring, representing the surge in syrupy asset investments.

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: [ Scalar art ] and depicting this scene: In a bustling Delaware diner, numerous truck drivers are seen enjoying stacks of freshly made pancakes, creating a busy and lively atmosphere. Meanwhile, on a nearby TV screen, Morgan Stanley's stock price (MS) is shown to be soaring, representing the surge in syrupy asset investments. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Style: Impressionist painting Description: In a picturesque Delaware diner, truck drivers are enjoying stacks of mouthwatering pancakes, creating a bustling and lively atmosphere. Bright colors and dynamic brushstrokes capture the energy of the scene, with syrup drizzling down the pancakes. Meanwhile, a TV screen in the background displays the soaring stock price of Morgan Stanley (MS), symbolizing the surge in syrupy asset investments. The painting evokes a sense of warmth, camaraderie, and the excitement of financial success.

*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 truck drivers in Delaware caused Morgan Stanley's stock price (MS) to increase. Make it feel plausible.

AI academic paper

(Because p < 0.01)
Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices

The Journal of Financial Trucking Studies

Jan 2024

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




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

Please draft the title and abstract of an academic research paper presenting the findings of the connection between The number of truck drivers in Delaware and Morgan Stanley's stock price (MS). 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 0.9704621 and p < 0.01 for 2010 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]]
Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices

[[ABSTRACT]]
This paper investigates the heretofore unexplored relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we employ rigorous statistical methods to analyze trends from 2010 to 2022. Our findings reveal a striking correlation coefficient of 0.9704621 and statistical significance with p < 0.01. Surprisingly, it seems that the movement of wheels in Delaware may influence the movement of MS stock prices. Our research sheds new light on the potential impact of seemingly unrelated factors on financial markets and paves the way for further investigation into the mysterious dance between the number of truck drivers and Wall Street's mood swings. So, the next time you see a truck passing by in Delaware, remember, it might just be steering your investment portfolio!


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

Please make up the introduction section of an academic research paper presenting the findings of the connection between The number of truck drivers in Delaware and Morgan Stanley's stock price (MS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices

[[ABSTRACT]]
This paper investigates the heretofore unexplored relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we employ rigorous statistical methods to analyze trends from 2010 to 2022. Our findings reveal a striking correlation coefficient of 0.9704621 and statistical significance with p < 0.01. Surprisingly, it seems that the movement of wheels in Delaware may influence the movement of MS stock prices. Our research sheds new light on the potential impact of seemingly unrelated factors on financial markets and paves the way for further investigation into the mysterious dance between the number of truck drivers and Wall Street's mood swings. So, the next time you see a truck passing by in Delaware, remember, it might just be steering your investment portfolio!

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
The intersection of finance and seemingly unrelated real-world factors has long been a matter of academic curiosity and speculation. The intricate web of relationships that may influence stock prices has led researchers to explore diverse and unexpected avenues of inquiry. One such unexpected avenue is the relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). As the old adage goes, "Where there's a wheel, there's a way," and with this in mind, we delve into the curious world of wheeled vehicles and Wall Street.

The trundling sound of trucks on Delaware's roadways may seem worlds apart from the hustle and bustle of financial markets, yet there may be a hidden connection eagerly waiting to be uncovered. Our study aims to unravel this enigma and shed light on the curious correlation between the wheels on the road and the ebbs and flows of stock prices. At first glance, one might question the rationale for examining such a seemingly far-fetched relationship. However, as history has taught us, the most groundbreaking discoveries often emerge from venturing into uncharted territory.

The confluence of seemingly unrelated variables has been the fodder for many a statistical adventure, but the conundrum of truck drivers and stock prices in the context of Delaware and Morgan Stanley offers a unique and whimsical puzzle to ponder. As we embark on this analytical journey, we invite the reader to join us in uncovering the unexpected twists and turns of this intriguing correlation. After all, in the world of statistical analysis, it's often the unlikeliest connections that yield the most intriguing insights. So buckle up, for this is no ordinary drive through the landscape of financial analysis. We are about to navigate the winding roads of statistical curiosity, en route to uncovering the uncanny dance between wheels and Wall Street. Let's hit the road!


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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of truck drivers in Delaware and Morgan Stanley's stock price (MS). 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices

[[ABSTRACT]]
This paper investigates the heretofore unexplored relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we employ rigorous statistical methods to analyze trends from 2010 to 2022. Our findings reveal a striking correlation coefficient of 0.9704621 and statistical significance with p < 0.01. Surprisingly, it seems that the movement of wheels in Delaware may influence the movement of MS stock prices. Our research sheds new light on the potential impact of seemingly unrelated factors on financial markets and paves the way for further investigation into the mysterious dance between the number of truck drivers and Wall Street's mood swings. So, the next time you see a truck passing by in Delaware, remember, it might just be steering your investment portfolio!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of finance and seemingly unrelated real-world factors has long been a matter of academic curiosity and speculation. The intricate web of relationships that may influence stock prices has led researchers to explore diverse and unexpected avenues of inquiry. One such unexpected avenue is the relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). As the old adage goes, "Where there's a wheel, there's a way," and with this in mind, we delve into the curious world of wheeled vehicles and Wall Street.
The trundling sound of trucks on Delaware's roadways may seem worlds apart from the hustle and bustle of financial markets, yet there may be a hidden connection eagerly waiting to be uncovered. Our study aims to unravel this enigma and shed light on the curious correlation between the wheels on the road and the ebbs and flows of stock prices. At first glance, one might question the rationale for examining such a seemingly far-fetched relationship. However, as history has taught us, the most groundbreaking discoveries often emerge from venturing into uncharted territory.
The confluence of seemingly unrelated variables has been the fodder for many a statistical adventure, but the conundrum of truck drivers and stock prices in the context of Delaware and Morgan Stanley offers a unique and whimsical puzzle to ponder. As we embark on this analytical journey, we invite the reader to join us in uncovering the unexpected twists and turns of this intriguing correlation. After all, in the world of statistical analysis, it's often the unlikeliest connections that yield the most intriguing insights. So buckle up, for this is no ordinary drive through the landscape of financial analysis. We are about to navigate the winding roads of statistical curiosity, en route to uncovering the uncanny dance between wheels and Wall Street. Let's hit the road!

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

A substantial body of literature exists on the relationship between various economic, demographic, and environmental factors and stock prices. Smith et al. (2018) explored the impact of weather patterns on stock market movements, finding that sunny days were associated with an increase in market activity, while rainy days were correlated with a decrease in trading volume. Drawing a parallel to our investigation, one might ponder whether the sun-drenched roads of Delaware are linked to Morgan Stanley's stock performance, yet that would be a sunny conclusion to jump to.

Doe and Jones (2020) delved into the influence of demographic shifts on stock prices, specifically focusing on the impact of population aging on market volatility. Their findings highlighted the potential significance of population dynamics in shaping financial markets, prompting us to consider whether the population of truck drivers in Delaware holds similar sway over MS stock prices, or if it's just a truckload of speculation.

Turning to more tangentially related sources, "The Trucking Industry and You" by Adams (2019) and "Financial Flourishes: Assessing Stock Market Trends" by Patel (2017) offer insights that, when viewed through the lens of our inquiry, seem to be on parallel tracks. While "The Grapes of Wrath" by John Steinbeck and "On the Road" by Jack Kerouac may seem like works of fiction, they provide an evocative portrayal of the life of truckers, and could there be a hidden message in their pages about the stock market? We may just be driving down a literary lane to nowhere.

Additionally, social media posts such as "Just saw a convoy of trucks zooming past, wonder if MS stock will do the same" and "Truck count in Delaware today: 387, are we about to witness a surge in MS stock prices?" have been spotted, suggesting that the collective consciousness is attuned to the potential link between truck activity and stock market movements. But maybe it's just all trucked up speculation.


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

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of truck drivers in Delaware and Morgan Stanley's stock price (MS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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 2010 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]]
Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices

[[ABSTRACT]]
This paper investigates the heretofore unexplored relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we employ rigorous statistical methods to analyze trends from 2010 to 2022. Our findings reveal a striking correlation coefficient of 0.9704621 and statistical significance with p < 0.01. Surprisingly, it seems that the movement of wheels in Delaware may influence the movement of MS stock prices. Our research sheds new light on the potential impact of seemingly unrelated factors on financial markets and paves the way for further investigation into the mysterious dance between the number of truck drivers and Wall Street's mood swings. So, the next time you see a truck passing by in Delaware, remember, it might just be steering your investment portfolio!

[[INTRODUCTION]]
The intersection of finance and seemingly unrelated real-world factors has long been a matter of academic curiosity and speculation. The intricate web of relationships that may influence stock prices has led researchers to explore diverse and unexpected avenues of inquiry. One such unexpected avenue is the relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). As the old adage goes, "Where there's a wheel, there's a way," and with this in mind, we delve into the curious world of wheeled vehicles and Wall Street.
The trundling sound of trucks on Delaware's roadways may seem worlds apart from the hustle and bustle of financial markets, yet there may be a hidden connection eagerly waiting to be uncovered. Our study aims to unravel this enigma and shed light on the curious correlation between the wheels on the road and the ebbs and flows of stock prices. At first glance, one might question the rationale for examining such a seemingly far-fetched relationship. However, as history has taught us, the most groundbreaking discoveries often emerge from venturing into uncharted territory.
The confluence of seemingly unrelated variables has been the fodder for many a statistical adventure, but the conundrum of truck drivers and stock prices in the context of Delaware and Morgan Stanley offers a unique and whimsical puzzle to ponder. As we embark on this analytical journey, we invite the reader to join us in uncovering the unexpected twists and turns of this intriguing correlation. After all, in the world of statistical analysis, it's often the unlikeliest connections that yield the most intriguing insights. So buckle up, for this is no ordinary drive through the landscape of financial analysis. We are about to navigate the winding roads of statistical curiosity, en route to uncovering the uncanny dance between wheels and Wall Street. Let's hit the road!

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To investigate the connection between the number of truck drivers in Delaware and the stock prices of Morgan Stanley (MS), we employed a range of methodological approaches to ensure robustness and reliability in our analysis. Our data collection spanned from 2010 to 2022, extracting information from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv). Before delving into the statistical nitty-gritty, it's worth noting that our research team spent a significant amount of time navigating the digital highways and byways of the internet, fending off the occasional pop-up detour and resisting the urge to click on cat videos.

To quantify the number of truck drivers in Delaware, we utilized a combination of official employment records and industry reports to capture the fluctuating workforce in the state. We also resisted the temptation to go on an impromptu road trip in a truck to personally count the drivers, opting instead for the more mundane approach of digital data acquisition. Our team's mechanical engineering enthusiasts were particularly disappointed by this decision.

Moving on to the stock price data of Morgan Stanley, we harnessed the power of financial market data from LSEG Analytics (Refinitiv) to trace the whirling highs and rumbling lows of MS stock over the selected timeframe. As we sifted through the sea of stock tickers and market indices, we couldn't help but wonder if the market's ups and downs were perhaps influenced by a cosmic game of financial musical chairs. Alas, our whimsical pondering was momentarily interrupted by the pressing need to analyze the data – a task we approached with the utmost seriousness and silliness in equal measure.

Having amassed the necessary datasets, we underwent a rigorous process of data cleaning, removing any rogue data points that dared to disrupt the harmony of our statistical symphony. Outliers were treated with the care and precision of a modern art restoration, delicately balancing the need to preserve the integrity of the dataset with the desire to showcase the quirks and anomalies of the financial world.

Once our data were polished and gleaming like a newly minted coin, we set about the task of statistical analysis. Employing a blend of regression models, time series analysis, and cross-correlation techniques, we endeavored to uncover the hidden patterns and rhythmic fluctuations between the number of truck drivers in Delaware and MS stock prices. We must admit, the research team enjoyed the occasional truck-themed pun to keep spirits high during the arduous statistical journey, much to the chagrin of our more gravely serious colleagues.

After performing the statistical tango with our datasets, we emerged with a correlation coefficient that left us in awe of the uncanny harmony between Delaware's wheeled denizens and the gyrations of Wall Street. The statistical significance of the correlation (p < 0.01) sent ripples of incredulity through the research team, who couldn't resist celebrating with an impromptu rendition of "Convoy" in the office. While the correlation was striking, we remained vigilant in acknowledging the limitations and caveats of our findings, fully aware that correlation does not imply causation – much to the dismay of our resident conspiracy theorist.

In the end, our research methodology sought to blend scientific rigor with a sprinkle of whimsy, recognizing that even the most serious of statistical inquiries can benefit from a dash of levity. As we bid adieu to the methodology section, we invite the reader to buckle up for the statistical joyride ahead, where the road is paved with data and the destination is a better understanding of the enigmatic relationship between truck drivers and Wall Street.


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

Please make up the results section of an academic research paper presenting the findings of the connection between The number of truck drivers in Delaware and Morgan Stanley's stock price (MS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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 2010 to 2022, you found a correlation 0.9704621, r-squared of 0.9417967, 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]]
Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices

[[ABSTRACT]]
This paper investigates the heretofore unexplored relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we employ rigorous statistical methods to analyze trends from 2010 to 2022. Our findings reveal a striking correlation coefficient of 0.9704621 and statistical significance with p < 0.01. Surprisingly, it seems that the movement of wheels in Delaware may influence the movement of MS stock prices. Our research sheds new light on the potential impact of seemingly unrelated factors on financial markets and paves the way for further investigation into the mysterious dance between the number of truck drivers and Wall Street's mood swings. So, the next time you see a truck passing by in Delaware, remember, it might just be steering your investment portfolio!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of finance and seemingly unrelated real-world factors has long been a matter of academic curiosity and speculation. The intricate web of relationships that may influence stock prices has led researchers to explore diverse and unexpected avenues of inquiry. One such unexpected avenue is the relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). As the old adage goes, "Where there's a wheel, there's a way," and with this in mind, we delve into the curious world of wheeled vehicles and Wall Street.
The trundling sound of trucks on Delaware's roadways may seem worlds apart from the hustle and bustle of financial markets, yet there may be a hidden connection eagerly waiting to be uncovered. Our study aims to unravel this enigma and shed light on the curious correlation between the wheels on the road and the ebbs and flows of stock prices. At first glance, one might question the rationale for examining such a seemingly far-fetched relationship. However, as history has taught us, the most groundbreaking discoveries often emerge from venturing into uncharted territory.
The confluence of seemingly unrelated variables has been the fodder for many a statistical adventure, but the conundrum of truck drivers and stock prices in the context of Delaware and Morgan Stanley offers a unique and whimsical puzzle to ponder. As we embark on this analytical journey, we invite the reader to join us in uncovering the unexpected twists and turns of this intriguing correlation. After all, in the world of statistical analysis, it's often the unlikeliest connections that yield the most intriguing insights. So buckle up, for this is no ordinary drive through the landscape of financial analysis. We are about to navigate the winding roads of statistical curiosity, en route to uncovering the uncanny dance between wheels and Wall Street. Let's hit the road!

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The statistical analysis conducted in this study revealed a remarkably strong correlation between the number of truck drivers in Delaware and the stock prices of Morgan Stanley (MS) from 2010 to 2022. The correlation coefficient of 0.9704621 indicates a robust positive relationship between these seemingly disparate variables. Additionally, the coefficient of determination (r-squared) of 0.9417967 underscores the high level of variability in MS stock prices that can be explained by the number of truck drivers in Delaware. Furthermore, the statistical significance with p < 0.01 indicates that the observed correlation is highly unlikely to have occurred by chance.

The results of this investigation are depicted in Figure 1, which presents a scatterplot illustrating the strong positive relationship between the number of truck drivers in Delaware and the stock prices of Morgan Stanley. The figure visually demonstrates the tight clustering of data points around a clear upward trend, highlighting the compelling association between these variables.

These findings offer intriguing insights into the potential influence of real-world factors, such as the presence of wheeled vehicles in a specific geographic region, on the dynamics of financial markets. The unexpected linkage between the number of truck drivers in Delaware and MS stock prices challenges conventional wisdom and underscores the complexity of stock market influences. This discovery opens the door to further exploration of the interconnectedness of seemingly unrelated elements and prompts a reevaluation of the factors driving stock price movements.

In conclusion, the results of this study provide compelling evidence of a strong correlation between the number of truck drivers in Delaware and the stock prices of Morgan Stanley. This unanticipated relationship invites reflection on the intricate and often enigmatic forces shaping financial markets. As the wheels of trucks continue to traverse the roadways of Delaware, it appears that they may also be steering the course of MS stock prices, making this an unexpected, yet revelatory, revelation in the world of financial analysis.


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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of truck drivers in Delaware and Morgan Stanley's stock price (MS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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]]
Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices

[[ABSTRACT]]
This paper investigates the heretofore unexplored relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we employ rigorous statistical methods to analyze trends from 2010 to 2022. Our findings reveal a striking correlation coefficient of 0.9704621 and statistical significance with p < 0.01. Surprisingly, it seems that the movement of wheels in Delaware may influence the movement of MS stock prices. Our research sheds new light on the potential impact of seemingly unrelated factors on financial markets and paves the way for further investigation into the mysterious dance between the number of truck drivers and Wall Street's mood swings. So, the next time you see a truck passing by in Delaware, remember, it might just be steering your investment portfolio!

[[LITERATURE REVIEW]]
A substantial body of literature exists on the relationship between various economic, demographic, and environmental factors and stock prices. Smith et al. (2018) explored the impact of weather patterns on stock market movements, finding that sunny days were associated with an increase in market activity, while rainy days were correlated with a decrease in trading volume. Drawing a parallel to our investigation, one might ponder whether the sun-drenched roads of Delaware are linked to Morgan Stanley's stock performance, yet that would be a sunny conclusion to jump to.
Doe and Jones (2020) delved into the influence of demographic shifts on stock prices, specifically focusing on the impact of population aging on market volatility. Their findings highlighted the potential significance of population dynamics in shaping financial markets, prompting us to consider whether the population of truck drivers in Delaware holds similar sway over MS stock prices, or if it's just a truckload of speculation.
Turning to more tangentially related sources, "The Trucking Industry and You" by Adams (2019) and "Financial Flourishes: Assessing Stock Market Trends" by Patel (2017) offer insights that, when viewed through the lens of our inquiry, seem to be on parallel tracks. While "The Grapes of Wrath" by John Steinbeck and "On the Road" by Jack Kerouac may seem like works of fiction, they provide an evocative portrayal of the life of truckers, and could there be a hidden message in their pages about the stock market? We may just be driving down a literary lane to nowhere.
Additionally, social media posts such as "Just saw a convoy of trucks zooming past, wonder if MS stock will do the same" and "Truck count in Delaware today: 387, are we about to witness a surge in MS stock prices?" have been spotted, suggesting that the collective consciousness is attuned to the potential link between truck activity and stock market movements. But maybe it's just all trucked up speculation.

[[RESULTS]]
The statistical analysis conducted in this study revealed a remarkably strong correlation between the number of truck drivers in Delaware and the stock prices of Morgan Stanley (MS) from 2010 to 2022. The correlation coefficient of 0.9704621 indicates a robust positive relationship between these seemingly disparate variables. Additionally, the coefficient of determination (r-squared) of 0.9417967 underscores the high level of variability in MS stock prices that can be explained by the number of truck drivers in Delaware. Furthermore, the statistical significance with p < 0.01 indicates that the observed correlation is highly unlikely to have occurred by chance.
The results of this investigation are depicted in Figure 1, which presents a scatterplot illustrating the strong positive relationship between the number of truck drivers in Delaware and the stock prices of Morgan Stanley. The figure visually demonstrates the tight clustering of data points around a clear upward trend, highlighting the compelling association between these variables.
These findings offer intriguing insights into the potential influence of real-world factors, such as the presence of wheeled vehicles in a specific geographic region, on the dynamics of financial markets. The unexpected linkage between the number of truck drivers in Delaware and MS stock prices challenges conventional wisdom and underscores the complexity of stock market influences. This discovery opens the door to further exploration of the interconnectedness of seemingly unrelated elements and prompts a reevaluation of the factors driving stock price movements.
In conclusion, the results of this study provide compelling evidence of a strong correlation between the number of truck drivers in Delaware and the stock prices of Morgan Stanley. This unanticipated relationship invites reflection on the intricate and often enigmatic forces shaping financial markets. As the wheels of trucks continue to traverse the roadways of Delaware, it appears that they may also be steering the course of MS stock prices, making this an unexpected, yet revelatory, revelation in the world of financial analysis.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The correlation found in our study between the number of truck drivers in Delaware and the stock prices of Morgan Stanley (MS) from 2010 to 2022 supports and expands upon previous literature exploring the unorthodox connections between seemingly unrelated factors and financial markets. Our findings align with Smith et al.'s (2018) revelation that sunny days spur market activity, suggesting that it's not just the sun that's driving market sentiment; maybe it's also the sound of truck horns echoing across the region. Furthermore, the work of Doe and Jones (2020) on demographic shifts and market volatility resonates with our results, as it seems that not only population aging but also the number of drivers in a specific location can rev up stock price movements.

Delving into more tangential sources, the writings of Adams (2019) and Patel (2017) seem to converge with our investigation, pointing to the convergence of the trucking industry and financial trends. Perhaps, hidden within the eloquent prose of Steinbeck and Kerouac lies a subtle hint about the impact of trucking on the stock market, lending credence to the notion that literature can be a highway to understanding financial phenomena. Even the social media buzz about truck convoys and stock surges, while seemingly whimsical, strikes a chord with our findings, albeit in a truckload of speculation.

Our results provide empirical backing to the notion that the presence of wheeled vehicles in a specific geographic region can indeed influence financial market dynamics. The substantial correlation coefficient and high coefficient of determination confirm that the number of truck drivers in Delaware plays a pivotal role in steering the variability of MS stock prices, challenging traditional views of market influences. It seems that the wheels of trucks not only churn the asphalt of Delaware but also propel the stock prices of MS, making it a tireless riddle in the realm of financial analysis.

This unexpected relationship tantalizingly beckons us to contemplate the intricate and often inscrutable forces affecting financial markets, reminding us that beneath the façade of traditional economic indicators lies a world of hidden connections waiting to be unveiled. Our study offers a compelling revelation that may have broader implications for financial analysis and prompts a reconsideration of the conventional wisdom surrounding market movements. As we reflect on the unexpected fusion of truck drivers and stock prices, it becomes evident that we may just be traversing a new path in the realm of financial research, where the road less traveled may indeed make all the difference.


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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of truck drivers in Delaware and Morgan Stanley's stock price (MS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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]]
Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices

[[ABSTRACT]]
This paper investigates the heretofore unexplored relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we employ rigorous statistical methods to analyze trends from 2010 to 2022. Our findings reveal a striking correlation coefficient of 0.9704621 and statistical significance with p < 0.01. Surprisingly, it seems that the movement of wheels in Delaware may influence the movement of MS stock prices. Our research sheds new light on the potential impact of seemingly unrelated factors on financial markets and paves the way for further investigation into the mysterious dance between the number of truck drivers and Wall Street's mood swings. So, the next time you see a truck passing by in Delaware, remember, it might just be steering your investment portfolio!

[[INTRDUCTION]]
The intersection of finance and seemingly unrelated real-world factors has long been a matter of academic curiosity and speculation. The intricate web of relationships that may influence stock prices has led researchers to explore diverse and unexpected avenues of inquiry. One such unexpected avenue is the relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). As the old adage goes, "Where there's a wheel, there's a way," and with this in mind, we delve into the curious world of wheeled vehicles and Wall Street.
The trundling sound of trucks on Delaware's roadways may seem worlds apart from the hustle and bustle of financial markets, yet there may be a hidden connection eagerly waiting to be uncovered. Our study aims to unravel this enigma and shed light on the curious correlation between the wheels on the road and the ebbs and flows of stock prices. At first glance, one might question the rationale for examining such a seemingly far-fetched relationship. However, as history has taught us, the most groundbreaking discoveries often emerge from venturing into uncharted territory.
The confluence of seemingly unrelated variables has been the fodder for many a statistical adventure, but the conundrum of truck drivers and stock prices in the context of Delaware and Morgan Stanley offers a unique and whimsical puzzle to ponder. As we embark on this analytical journey, we invite the reader to join us in uncovering the unexpected twists and turns of this intriguing correlation. After all, in the world of statistical analysis, it's often the unlikeliest connections that yield the most intriguing insights. So buckle up, for this is no ordinary drive through the landscape of financial analysis. We are about to navigate the winding roads of statistical curiosity, en route to uncovering the uncanny dance between wheels and Wall Street. Let's hit the road!

[[RESULTS]]
The statistical analysis conducted in this study revealed a remarkably strong correlation between the number of truck drivers in Delaware and the stock prices of Morgan Stanley (MS) from 2010 to 2022. The correlation coefficient of 0.9704621 indicates a robust positive relationship between these seemingly disparate variables. Additionally, the coefficient of determination (r-squared) of 0.9417967 underscores the high level of variability in MS stock prices that can be explained by the number of truck drivers in Delaware. Furthermore, the statistical significance with p < 0.01 indicates that the observed correlation is highly unlikely to have occurred by chance.
The results of this investigation are depicted in Figure 1, which presents a scatterplot illustrating the strong positive relationship between the number of truck drivers in Delaware and the stock prices of Morgan Stanley. The figure visually demonstrates the tight clustering of data points around a clear upward trend, highlighting the compelling association between these variables.
These findings offer intriguing insights into the potential influence of real-world factors, such as the presence of wheeled vehicles in a specific geographic region, on the dynamics of financial markets. The unexpected linkage between the number of truck drivers in Delaware and MS stock prices challenges conventional wisdom and underscores the complexity of stock market influences. This discovery opens the door to further exploration of the interconnectedness of seemingly unrelated elements and prompts a reevaluation of the factors driving stock price movements.
In conclusion, the results of this study provide compelling evidence of a strong correlation between the number of truck drivers in Delaware and the stock prices of Morgan Stanley. This unanticipated relationship invites reflection on the intricate and often enigmatic forces shaping financial markets. As the wheels of trucks continue to traverse the roadways of Delaware, it appears that they may also be steering the course of MS stock prices, making this an unexpected, yet revelatory, revelation in the world of financial analysis.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation has brought to light an unexpectedly robust correlation between the number of truck drivers in Delaware and the stock prices of Morgan Stanley (MS). The striking correlation coefficient of 0.9704621 and high coefficient of determination indicate a compelling association that defies conventional expectations. The unearthing of such a profound relationship between the trundling wheels of Delaware and the ebbs and flows of MS stock prices underscores the whimsical nature of statistical analysis in unraveling enigmatic connections.

While our findings may seem like a wheel-y wild discovery, they provoke contemplation on the intricate web of influences that shape financial markets. The compelling evidence presented here beckons us to reexamine the conventional paradigms of stock price determinants and consider the unlikeliest of contenders in the grand scheme of market dynamics. Indeed, the next time you encounter a convoy of trucks navigating Delaware's roadways, you might just be witnessing the subtle yet profound influence on Wall Street's mood swings!

As we bring this curious journey to a close, it becomes clear that the allure of statistical exploration lies in uncovering the unexpected and delving into uncharted territories. We invite fellow researchers to heed our findings and refrain from reinventing the wheel, for it seems that the wheels in motion in Delaware are already steering the course of MS stock prices. So, let us bid adieu to further research in this peculiar arena, as the correlation between wheels and Wall Street has been illuminated with resounding clarity.


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 Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Financial Trucking Studies


Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Trucking and Financial 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 Morgan Stanley and Delaware's Wheeled Rally: The Correlation Between Truck Drivers and MS Stock Prices with an abstract of This paper investigates the heretofore unexplored relationship between the number of truck drivers in the state of Delaware and the stock prices of Morgan Stanley (MS). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we employ rigorous statistical methods to analyze trends from 2010 to 2022. Our findings reveal a striking correlation coefficient of 0.9704621 and statistical significance with p < 0.01. Surprisingly, it seems that the movement of wheels in Delaware may influence the movement of MS stock prices. Our research sheds new light on the potential impact of seemingly unrelated factors on financial markets and paves the way for further investigation into the mysterious dance between the number of truck drivers and Wall Street's mood swings. So, the next time you see a truck passing by in Delaware, remember, it might just be steering your investment portfolio!

ChatGPT:

Morgan Stanley, Delaware, truck drivers, stock prices, correlation, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, statistical methods, trends, financial markets, Wall Street, investment portfolio

*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 truck drivers in Delaware
Detailed data title: BLS estimate of heavy and tractor-trailer truck drivers in Delaware
Source: Bureau of Larbor Statistics
See what else correlates with The number of truck drivers in Delaware

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

See what else correlates with Morgan Stanley's stock price (MS)

Correlation r = 0.9704621 (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 = 0.9417967 (Coefficient of determination)
This means 94.2% of the change in the one variable (i.e., Morgan Stanley's stock price (MS)) is predictable based on the change in the other (i.e., The number of truck drivers in Delaware) over the 13 years from 2010 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.9E-8. 0.0000000388666299699107500000
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 0.97 in 3.9E-6% of random cases. Said differently, if you correlated 25,729,012 random variables You don't actually need 25 million 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 12 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 12 because we have two variables measured over a period of 13 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.

[ 0.9, 0.99 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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


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

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
2010201120122013201420152016201720182019202020212022
The number of truck drivers in Delaware (Laborers)3560324031303400376037903910411043704270451050407240
Morgan Stanley's stock price (MS) (Stock price)30.727.6315.7620.1631.3339.0530.743.0952.7639.0251.269.2899.33




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([3560,3240,3130,3400,3760,3790,3910,4110,4370,4270,4510,5040,7240,])
array_2 = np.array([30.7,27.63,15.76,20.16,31.33,39.05,30.7,43.09,52.76,39.02,51.2,69.28,99.33,])
array_1_name = "The number of truck drivers in Delaware"
array_2_name = "Morgan Stanley's stock price (MS)"

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

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



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You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

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Correlation ID: 3266 · Black Variable ID: 17929 · Red Variable ID: 1600
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