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Spurious correlation #1,732 · 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 legislators in Alaska and the second variable is Tesla's stock price (TSLA).  The chart goes from 2011 to 2021, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As the number of legislators in Alaska increases, so does the demand for heated seats in electric cars to combat the chilly political debates. Tesla, being a prominent electric car manufacturer, experiences a surge in interest and orders for their vehicles, leading to a boost in stock price. This is a classic case of legislative decisions sparking a heated market for electric car features!

Model: dalle-3
Prompt: Create a comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s, depicting a scene in a room in the Alaska State Capitol. In the room, 60 legislators are gathered around a large table, engaged in discussions about future policies and regulations. A nearby screen displays the current Tesla stock price, fluctuating as the legislators continue their discussions. The room is dimly lit, with shadows adding depth to the scene, and the legislators are drawn with exaggerated, expressive features to capture their animated conversation. 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 legislators in Alaska and Tesla's stock price. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Sci-fi fantacy An image that looks like it is a scene from a Pixar movie Cute 2D animation A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s 3D Pixar Style
ChatGPT: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s

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 legislators in Alaska and Tesla's stock price together. Keep the description simple.
ChatGPT: In a room in the Alaska State Capitol, 60 legislators are gathered, discussing future policies and regulations. On a nearby screen, the current Tesla stock price is displayed, fluctuating as the legislators continue their discussions.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s ] and depicting this scene: In a room in the Alaska State Capitol, 60 legislators are gathered, discussing future policies and regulations. On a nearby screen, the current Tesla stock price is displayed, fluctuating as the legislators continue their discussions. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create a comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s, depicting a scene in a room in the Alaska State Capitol. In the room, 60 legislators are gathered around a large table, engaged in discussions about future policies and regulations. A nearby screen displays the current Tesla stock price, fluctuating as the legislators continue their discussions. The room is dimly lit, with shadows adding depth to the scene, and the legislators are drawn with exaggerated, expressive features to capture their animated conversation.

*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 legislators in Alaska caused Tesla's stock price (TSLA) to increase.

AI academic paper

(Because p < 0.01)
Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price

The Journal of Legislative Lightning

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

Please draft the title and abstract of an academic research paper presenting the findings of the connection between The number of legislators in Alaska and Tesla's stock price (TSLA). Make lots of jokes, goofy observations, and puns.

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

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 0.9711894 and p < 0.01 for 2011 to 2021.

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]]
"Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price"

[[ABSTRACT]]
This research paper delves into the surprising connection between the number of legislators in Alaska and the fluctuating stock price of Tesla (TSLA) over the past decade. With a diligent examination of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) from 2011 to 2021, our team uncovered a striking correlation coefficient of 0.9711894 and a p-value less than 0.01. Despite the seemingly incongruous nature of these two variables, our findings shed light on how legislative developments in Alaska may have had a shockingly electrifying impact on TSLA stock. So, let's strap in for an exhilarating journey through the peaks and valleys of Alaskan lawmakers and Tesla's stock prices, as we uncover the legislative current that appears to resonate with the market currents.


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

Please make up the introduction section of an academic research paper presenting the findings of the connection between The number of legislators in Alaska and Tesla's stock price (TSLA). Make lots of jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
"Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price"

[[ABSTRACT]]
This research paper delves into the surprising connection between the number of legislators in Alaska and the fluctuating stock price of Tesla (TSLA) over the past decade. With a diligent examination of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) from 2011 to 2021, our team uncovered a striking correlation coefficient of 0.9711894 and a p-value less than 0.01. Despite the seemingly incongruous nature of these two variables, our findings shed light on how legislative developments in Alaska may have had a shockingly electrifying impact on TSLA stock. So, let's strap in for an exhilarating journey through the peaks and valleys of Alaskan lawmakers and Tesla's stock prices, as we uncover the legislative current that appears to resonate with the market currents.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
INTRODUCTION

In the ever-evolving world of finance and legislation, some connections might seem more unusual than a moose browsing in downtown Anchorage. However, our research aims to unravel a correlation that is as shocking as a bolt of lightning - the surprising relationship between the number of legislators in Alaska and the electrifying effects on Tesla's stock price (TSLA). While it may seem as unexpected as a walrus sunbathing on an iceberg, our findings suggest that the legislative landscape in Alaska may have a more electrifying impact on TSLA stock than previously imagined.

As we delve into this uncharted territory, it's important to acknowledge that the link between legislative activity in the 49th state and the stock market might initially appear as incongruous as catching a salmon in the Sahara. However, with a keen eye for data analysis and a pinch of humor, our team has uncovered a correlation coefficient so potent, it could power a whole lineup of electric vehicles. Our results demonstrate a striking correlation coefficient of 0.9711894, leaving us more stunned than a tourist spotting a wild bear from their RV.

So, buckle up for an electrifying journey through time, data, and legislative theaters, as we uncover the peculiar but undeniable connection between the things Alaskan lawmakers say and do, and the shocking fluctuations in TSLA's stock price. Let's rev our engines and prepare for a ride that's as wild as a group of caribou racing through the tundra – we promise it will be a thrill, even if it's a bit of a wild ride!


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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of legislators in Alaska and Tesla's stock price (TSLA). Make lots of jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
"Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price"

[[ABSTRACT]]
This research paper delves into the surprising connection between the number of legislators in Alaska and the fluctuating stock price of Tesla (TSLA) over the past decade. With a diligent examination of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) from 2011 to 2021, our team uncovered a striking correlation coefficient of 0.9711894 and a p-value less than 0.01. Despite the seemingly incongruous nature of these two variables, our findings shed light on how legislative developments in Alaska may have had a shockingly electrifying impact on TSLA stock. So, let's strap in for an exhilarating journey through the peaks and valleys of Alaskan lawmakers and Tesla's stock prices, as we uncover the legislative current that appears to resonate with the market currents.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
In the ever-evolving world of finance and legislation, some connections might seem more unusual than a moose browsing in downtown Anchorage. However, our research aims to unravel a correlation that is as shocking as a bolt of lightning - the surprising relationship between the number of legislators in Alaska and the electrifying effects on Tesla's stock price (TSLA). While it may seem as unexpected as a walrus sunbathing on an iceberg, our findings suggest that the legislative landscape in Alaska may have a more electrifying impact on TSLA stock than previously imagined.
As we delve into this uncharted territory, it's important to acknowledge that the link between legislative activity in the 49th state and the stock market might initially appear as incongruous as catching a salmon in the Sahara. However, with a keen eye for data analysis and a pinch of humor, our team has uncovered a correlation coefficient so potent, it could power a whole lineup of electric vehicles. Our results demonstrate a striking correlation coefficient of 0.9711894, leaving us more stunned than a tourist spotting a wild bear from their RV.
So, buckle up for an electrifying journey through time, data, and legislative theaters, as we uncover the peculiar but undeniable connection between the things Alaskan lawmakers say and do, and the shocking fluctuations in TSLA's stock price. Let's rev our engines and prepare for a ride that's as wild as a group of caribou racing through the tundra – we promise it will be a thrill, even if it's a bit of a wild ride!

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The relationship between legislative activity and stock prices has been a subject of considerable interest to researchers in the fields of finance and economics. Smith et al. (2018) explored the influence of political events on stock market movements, uncovering significant correlations in various state legislatures. Similarly, Doe and Jones (2015) delved into the impact of legislative decisions on specific industry stocks, shedding light on the intriguing interplay between legislative dynamics and market behavior.

However, as we venture into the uncharted, snow-covered terrain of Alaskan lawmakers and Tesla's stock price (TSLA), we find ourselves in a rather unexpected place, not unlike stumbling upon a diamond in the rough – or more aptly, a moose in a ski lodge. It's important to acknowledge that, while the academic landscape may often be as serious as a polar bear on thin ice, we are, in fact, leafing through studies on lawmaking and finance to uncover a correlation that's as shocking as a bug zapper in the Arctic.

As we journey through this academic tundra, we can't help but consider the works of non-fiction authors who have explored the intricate dance of legislation and financial markets. In "The Alaskan Experience: Politics and Economics at the Edge of the Frontier" by John Smith, the author delves into the fascinating world of Alaskan politics and its potential impact on the state's economic landscape, offering a backdrop to our peculiar exploration. In "Stocks and the State: How Legislative Decisions Shape Market Dynamics" by Jane Doe, we find an insightful exploration of the legislative factors that can send ripples through financial markets, offering a contextually relevant perspective to our investigation.

But why stop at the realm of non-fiction? Fictional works, although often dismissed as flights of fancy, can surprisingly offer analogies and perspectives that resonate with real-world phenomena. In "The Shocking Saga of Stocks: A Financial Fantasy" by L. M. Jones, the author crafts a whimsical tale of a magical marketplace where legislative whispers hold the power to sway stock prices. Similarly, in "Lightning Legislation: Tales of Lawmaking and Financial Feats" by J.K. Smith, the reader is taken on a fantastical journey through a world where legislative decisions spark astonishing market movements, offering a playful reflection of our own surprising findings.

And as we embrace the spirit of unconventional connections, we can't overlook the influence of childhood cartoons and shows that may have subtly shaped our perspectives on legislative power and market shocks. From the resilient teamwork in "Paw Patrol" to the surprising twists and turns in "SpongeBob SquarePants," we've been subconsciously primed to appreciate the unexpected effects of legislative action, much like the quirky occurrences we encounter in our current investigation.

At this juncture, it becomes abundantly clear that our investigation isn't just about numbers and data; it's about embracing the unexpected, finding amusement in the incongruous, and uncovering the delightful correlations that make research as electrifying as a lightning storm in the Last Frontier.


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

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of legislators in Alaska and Tesla's stock price (TSLA). Make lots of 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 2011 to 2021

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]]
"Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price"

[[ABSTRACT]]
This research paper delves into the surprising connection between the number of legislators in Alaska and the fluctuating stock price of Tesla (TSLA) over the past decade. With a diligent examination of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) from 2011 to 2021, our team uncovered a striking correlation coefficient of 0.9711894 and a p-value less than 0.01. Despite the seemingly incongruous nature of these two variables, our findings shed light on how legislative developments in Alaska may have had a shockingly electrifying impact on TSLA stock. So, let's strap in for an exhilarating journey through the peaks and valleys of Alaskan lawmakers and Tesla's stock prices, as we uncover the legislative current that appears to resonate with the market currents.

[[INTRODUCTION]]
INTRODUCTION
In the ever-evolving world of finance and legislation, some connections might seem more unusual than a moose browsing in downtown Anchorage. However, our research aims to unravel a correlation that is as shocking as a bolt of lightning - the surprising relationship between the number of legislators in Alaska and the electrifying effects on Tesla's stock price (TSLA). While it may seem as unexpected as a walrus sunbathing on an iceberg, our findings suggest that the legislative landscape in Alaska may have a more electrifying impact on TSLA stock than previously imagined.
As we delve into this uncharted territory, it's important to acknowledge that the link between legislative activity in the 49th state and the stock market might initially appear as incongruous as catching a salmon in the Sahara. However, with a keen eye for data analysis and a pinch of humor, our team has uncovered a correlation coefficient so potent, it could power a whole lineup of electric vehicles. Our results demonstrate a striking correlation coefficient of 0.9711894, leaving us more stunned than a tourist spotting a wild bear from their RV.
So, buckle up for an electrifying journey through time, data, and legislative theaters, as we uncover the peculiar but undeniable connection between the things Alaskan lawmakers say and do, and the shocking fluctuations in TSLA's stock price. Let's rev our engines and prepare for a ride that's as wild as a group of caribou racing through the tundra – we promise it will be a thrill, even if it's a bit of a wild ride!

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

Data Collection:
To embark on our exhilarating quest for uncovering the nuanced relationship between the number of legislators in the Last Frontier and the electrifying rollercoaster that is Tesla's stock price, our research team scoured the digital landscape at a speed brisker than an Iditarod sled dog in peak form. We tapped into a diverse array of resources, browsing through the untamed wilderness of the internet with the precision of a bear on the hunt for wild salmon. Specifically, we relied heavily on data from the Bureau of Labor Statistics and the insightful analytics provided by LSEG Analytics (Refinitiv), spanning the years from 2011 to 2021.

Unconventional Data Retrieval:
In our pursuit of unearthly correlations and unexpected revelations, we utilized a technique so unconventional that it makes tracking your expenses look like a leisurely stroll in a manicured garden. We dusted off the mystical art of "datalogical thumb wrestling,” where we engaged in a ferocious battle of wits with the data streams, emerging victorious with the most reliable and robust datasets, as if we were wrestling with a Kodiak bear but without the imminent danger.

Cross-Domain Analysis:
To bridge the realms of legislative intricacies and the tempestuous tides of stock market fluctuations, we employed an innovative approach reminiscent of sleuthing in the Alaskan wilderness. We fused the precision of a seasoned tracker with the agility of a stock market ninja, conducting a cross-domain analysis that harmonized the legislative calendar in Alaska with Tesla's stock performance. This fusion of two seemingly disparate disciplines resulted in a deluge of eye-opening insights, akin to discovering a hidden cavern of aurora borealis gems within the financial and legislative landscapes.

Statistical Jiu-Jitsu:
Upon gathering the treasure trove of data, our team performed statistical jiu-jitsu, with our equations executing maneuvers more jaw-dropping than a Cirque du Soleil performance. By leveraging an arsenal of sophisticated statistical techniques, including regression analysis and time series modeling, we meticulously teased out the intricate dance between Alaskan lawmakers and the mesmerizing movements of TSLA stock prices. The mathematical prowess displayed in our analysis would make even the savviest numerologist tip their hat in respect.

Validity Checks:
To ensure the reliability of our findings and validate the magnitude of the connection discovered, we subjected our results to the rigorous scrutiny of validity checks. This involved testing the robustness of our correlation against a barrage of hypothetical scenarios, akin to stress-testing a snow shelter in the face of an Alaskan blizzard. This rigorous process fortified the veracity of our correlation and affirmed the comprehensive nature of our exploration.

Incorporating the Element of Surprise:
Lastly, and perhaps most importantly, we injected a generous dose of humor and wit into our methodology, infusing the otherwise scholarly pursuit with a lighthearted charisma that made the research process as engaging as a midnight sun festival. After all, every engaging research endeavor could benefit from a sprinkle of unexpected hilarity.

Our enthusiastic approach to methodologically dissecting the unorthodox relationship between the political pyramid of Alaska and the sizzling stock performance of TSLA laid the foundation for our hair-raising findings. With our methodological arsenal fully equipped, we charged headfirst into the unknown, extracting revelations that are as mesmerizing as glimpsing the Northern Lights from the comfort of a cozy cabin.


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

Please make up the results section of an academic research paper presenting the findings of the connection between The number of legislators in Alaska and Tesla's stock price (TSLA). Make lots of 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 2011 to 2021, you found a correlation 0.9711894, r-squared of 0.9432089, 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]]
"Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price"

[[ABSTRACT]]
This research paper delves into the surprising connection between the number of legislators in Alaska and the fluctuating stock price of Tesla (TSLA) over the past decade. With a diligent examination of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) from 2011 to 2021, our team uncovered a striking correlation coefficient of 0.9711894 and a p-value less than 0.01. Despite the seemingly incongruous nature of these two variables, our findings shed light on how legislative developments in Alaska may have had a shockingly electrifying impact on TSLA stock. So, let's strap in for an exhilarating journey through the peaks and valleys of Alaskan lawmakers and Tesla's stock prices, as we uncover the legislative current that appears to resonate with the market currents.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
In the ever-evolving world of finance and legislation, some connections might seem more unusual than a moose browsing in downtown Anchorage. However, our research aims to unravel a correlation that is as shocking as a bolt of lightning - the surprising relationship between the number of legislators in Alaska and the electrifying effects on Tesla's stock price (TSLA). While it may seem as unexpected as a walrus sunbathing on an iceberg, our findings suggest that the legislative landscape in Alaska may have a more electrifying impact on TSLA stock than previously imagined.
As we delve into this uncharted territory, it's important to acknowledge that the link between legislative activity in the 49th state and the stock market might initially appear as incongruous as catching a salmon in the Sahara. However, with a keen eye for data analysis and a pinch of humor, our team has uncovered a correlation coefficient so potent, it could power a whole lineup of electric vehicles. Our results demonstrate a striking correlation coefficient of 0.9711894, leaving us more stunned than a tourist spotting a wild bear from their RV.
So, buckle up for an electrifying journey through time, data, and legislative theaters, as we uncover the peculiar but undeniable connection between the things Alaskan lawmakers say and do, and the shocking fluctuations in TSLA's stock price. Let's rev our engines and prepare for a ride that's as wild as a group of caribou racing through the tundra – we promise it will be a thrill, even if it's a bit of a wild ride!

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

Our analysis of the relationship between the number of legislators in Alaska and Tesla's stock price (TSLA) from 2011 to 2021 yielded some electrifying results that even Nikola Tesla himself might find shocking! With a correlation coefficient of 0.9711894 and an r-squared of 0.9432089, our findings indicate a strikingly strong positive correlation between these two seemingly disparate variables. This correlation is so impressive it could power a whole fleet of electric vehicles!

Fig. 1 visually encapsulates this eye-opening correlation, illustrating a scatterplot that resembles a lightning bolt leaping from the Alaskan legislative chambers to the stock market floor. It's a sight that will surely make anyone's hair stand on end – a bit like those energetic electrons buzzing in a Tesla coil!

The p-value of less than 0.01 further cements the significance of this relationship, strengthening the argument that the number of legislators in Alaska can indeed have a jolting impact on TSLA stock prices. This discovery highlights a fascinating marriage of state-level policy decisions and market reactions, proving that the legislative process is not just bureaucracy – it's a current that can surge through the stock market, potentially electrifying investor sentiment.

Our results support the notion that shifts in the Alaskan legislative landscape may not just shape state policy but also send shockwaves through the financial markets, specifically the value of TSLA stock. It seems that the decisions made in the Last Frontier can radiate far beyond its borders, affecting the value of a company that's revolutionizing transportation as we know it. It's as though the legislation and stock prices are engaged in a dance as wild and unpredictable as an Alaskan winter storm – showing that the markets are not only influenced by global economic forces but also by local political events.

In conclusion, our findings suggest that while individuals may be polar bears apart in their thinking about state policy and the stock market, the data reveals a current of connection that could rival the mighty rivers of the 49th state. The implications of our research extend as far and wide as the sprawling Alaskan landscape, demonstrating the potential for state-level decisions to unleash a force as powerful as the Northern Lights on the stock market stage.


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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of legislators in Alaska and Tesla's stock price (TSLA). Make lots of 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]]
"Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price"

[[ABSTRACT]]
This research paper delves into the surprising connection between the number of legislators in Alaska and the fluctuating stock price of Tesla (TSLA) over the past decade. With a diligent examination of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) from 2011 to 2021, our team uncovered a striking correlation coefficient of 0.9711894 and a p-value less than 0.01. Despite the seemingly incongruous nature of these two variables, our findings shed light on how legislative developments in Alaska may have had a shockingly electrifying impact on TSLA stock. So, let's strap in for an exhilarating journey through the peaks and valleys of Alaskan lawmakers and Tesla's stock prices, as we uncover the legislative current that appears to resonate with the market currents.

[[LITERATURE REVIEW]]
The relationship between legislative activity and stock prices has been a subject of considerable interest to researchers in the fields of finance and economics. Smith et al. (2018) explored the influence of political events on stock market movements, uncovering significant correlations in various state legislatures. Similarly, Doe and Jones (2015) delved into the impact of legislative decisions on specific industry stocks, shedding light on the intriguing interplay between legislative dynamics and market behavior.
However, as we venture into the uncharted, snow-covered terrain of Alaskan lawmakers and Tesla's stock price (TSLA), we find ourselves in a rather unexpected place, not unlike stumbling upon a diamond in the rough – or more aptly, a moose in a ski lodge. It's important to acknowledge that, while the academic landscape may often be as serious as a polar bear on thin ice, we are, in fact, leafing through studies on lawmaking and finance to uncover a correlation that's as shocking as a bug zapper in the Arctic.
As we journey through this academic tundra, we can't help but consider the works of non-fiction authors who have explored the intricate dance of legislation and financial markets. In "The Alaskan Experience: Politics and Economics at the Edge of the Frontier" by John Smith, the author delves into the fascinating world of Alaskan politics and its potential impact on the state's economic landscape, offering a backdrop to our peculiar exploration. In "Stocks and the State: How Legislative Decisions Shape Market Dynamics" by Jane Doe, we find an insightful exploration of the legislative factors that can send ripples through financial markets, offering a contextually relevant perspective to our investigation.
But why stop at the realm of non-fiction? Fictional works, although often dismissed as flights of fancy, can surprisingly offer analogies and perspectives that resonate with real-world phenomena. In "The Shocking Saga of Stocks: A Financial Fantasy" by L. M. Jones, the author crafts a whimsical tale of a magical marketplace where legislative whispers hold the power to sway stock prices. Similarly, in "Lightning Legislation: Tales of Lawmaking and Financial Feats" by J.K. Smith, the reader is taken on a fantastical journey through a world where legislative decisions spark astonishing market movements, offering a playful reflection of our own surprising findings.
And as we embrace the spirit of unconventional connections, we can't overlook the influence of childhood cartoons and shows that may have subtly shaped our perspectives on legislative power and market shocks. From the resilient teamwork in "Paw Patrol" to the surprising twists and turns in "SpongeBob SquarePants," we've been subconsciously primed to appreciate the unexpected effects of legislative action, much like the quirky occurrences we encounter in our current investigation.
At this juncture, it becomes abundantly clear that our investigation isn't just about numbers and data; it's about embracing the unexpected, finding amusement in the incongruous, and uncovering the delightful correlations that make research as electrifying as a lightning storm in the Last Frontier.

[[RESULTS]]
Our analysis of the relationship between the number of legislators in Alaska and Tesla's stock price (TSLA) from 2011 to 2021 yielded some electrifying results that even Nikola Tesla himself might find shocking! With a correlation coefficient of 0.9711894 and an r-squared of 0.9432089, our findings indicate a strikingly strong positive correlation between these two seemingly disparate variables. This correlation is so impressive it could power a whole fleet of electric vehicles!
Fig. 1 visually encapsulates this eye-opening correlation, illustrating a scatterplot that resembles a lightning bolt leaping from the Alaskan legislative chambers to the stock market floor. It's a sight that will surely make anyone's hair stand on end – a bit like those energetic electrons buzzing in a Tesla coil!
The p-value of less than 0.01 further cements the significance of this relationship, strengthening the argument that the number of legislators in Alaska can indeed have a jolting impact on TSLA stock prices. This discovery highlights a fascinating marriage of state-level policy decisions and market reactions, proving that the legislative process is not just bureaucracy – it's a current that can surge through the stock market, potentially electrifying investor sentiment.
Our results support the notion that shifts in the Alaskan legislative landscape may not just shape state policy but also send shockwaves through the financial markets, specifically the value of TSLA stock. It seems that the decisions made in the Last Frontier can radiate far beyond its borders, affecting the value of a company that's revolutionizing transportation as we know it. It's as though the legislation and stock prices are engaged in a dance as wild and unpredictable as an Alaskan winter storm – showing that the markets are not only influenced by global economic forces but also by local political events.
In conclusion, our findings suggest that while individuals may be polar bears apart in their thinking about state policy and the stock market, the data reveals a current of connection that could rival the mighty rivers of the 49th state. The implications of our research extend as far and wide as the sprawling Alaskan landscape, demonstrating the potential for state-level decisions to unleash a force as powerful as the Northern Lights on the stock market stage.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our findings provide compelling evidence of a shockingly strong positive correlation between the number of legislators in Alaska and Tesla's stock price, adding a striking twist to the relationship between legislative activity and financial markets. In the scholarly literature, light-hearted analogies and unconventional connections have often been dismissed as mere flights of fancy, much like a snowball in the face, but our research emphatically demonstrates that even the quirkiest of associations can hold significant real-world implications.

Drawing inspiration from the whimsical "Lightning Legislation: Tales of Lawmaking and Financial Feats" by J.K. Smith, we've discovered that the impact of Alaskan legislative decisions on TSLA stock can indeed be as electrifying as a magical marketplace of legislative whispers. It seems that the shockingly strong correlation between these seemingly disparate variables may be akin to the awe-inspiring phenomena in fictional narratives – a testament to the unexpected ways in which real-world events can mirror the imaginative.

As we integrate the tangible world with playful reflections, it's evident that our findings align with the peculiar juxtapositions found in these non-traditional sources. Much like the surprising twists and turns in "SpongeBob SquarePants," the relationship between Alaskan lawmakers and TSLA stock prices has defied conventional expectations, highlighting the delightful correlations that make research as electrifying as a lightning storm in the Last Frontier.

Moreover, our results affirm the work of Smith et al. (2018) and Doe and Jones (2015), who explored the influence of political events and legislative decisions on stock market movements. Just as they uncovered significant correlations between political events and market behavior, our research has unveiled a notably robust correlation specific to the state of Alaska and the electric jolts it appears to send through TSLA stock. Therefore, our findings lend empirical support to the notion that local legislative decisions can have a tangible impact on specific industry stocks, with Alaska serving as a captivating case study.

In a world increasingly dictated by data and numbers, our study offers a refreshing reminder that the unexpected correlations and juxtapositions found in fictional tales, childhood cartoons, and even the quirky sensibilities of academic exploration have undeniable parallels in the realm of finance and legislative dynamics. Our work underscores the importance of embracing the unconventional, finding delight in the incongruous, and recognizing that the most surprising connections can have real-world implications as electrifying as a Tesla coil in a winter storm.


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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of legislators in Alaska and Tesla's stock price (TSLA). Make lots of 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]]
"Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price"

[[ABSTRACT]]
This research paper delves into the surprising connection between the number of legislators in Alaska and the fluctuating stock price of Tesla (TSLA) over the past decade. With a diligent examination of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) from 2011 to 2021, our team uncovered a striking correlation coefficient of 0.9711894 and a p-value less than 0.01. Despite the seemingly incongruous nature of these two variables, our findings shed light on how legislative developments in Alaska may have had a shockingly electrifying impact on TSLA stock. So, let's strap in for an exhilarating journey through the peaks and valleys of Alaskan lawmakers and Tesla's stock prices, as we uncover the legislative current that appears to resonate with the market currents.

[[INTRDUCTION]]
INTRODUCTION
In the ever-evolving world of finance and legislation, some connections might seem more unusual than a moose browsing in downtown Anchorage. However, our research aims to unravel a correlation that is as shocking as a bolt of lightning - the surprising relationship between the number of legislators in Alaska and the electrifying effects on Tesla's stock price (TSLA). While it may seem as unexpected as a walrus sunbathing on an iceberg, our findings suggest that the legislative landscape in Alaska may have a more electrifying impact on TSLA stock than previously imagined.
As we delve into this uncharted territory, it's important to acknowledge that the link between legislative activity in the 49th state and the stock market might initially appear as incongruous as catching a salmon in the Sahara. However, with a keen eye for data analysis and a pinch of humor, our team has uncovered a correlation coefficient so potent, it could power a whole lineup of electric vehicles. Our results demonstrate a striking correlation coefficient of 0.9711894, leaving us more stunned than a tourist spotting a wild bear from their RV.
So, buckle up for an electrifying journey through time, data, and legislative theaters, as we uncover the peculiar but undeniable connection between the things Alaskan lawmakers say and do, and the shocking fluctuations in TSLA's stock price. Let's rev our engines and prepare for a ride that's as wild as a group of caribou racing through the tundra – we promise it will be a thrill, even if it's a bit of a wild ride!

[[RESULTS]]
Our analysis of the relationship between the number of legislators in Alaska and Tesla's stock price (TSLA) from 2011 to 2021 yielded some electrifying results that even Nikola Tesla himself might find shocking! With a correlation coefficient of 0.9711894 and an r-squared of 0.9432089, our findings indicate a strikingly strong positive correlation between these two seemingly disparate variables. This correlation is so impressive it could power a whole fleet of electric vehicles!
Fig. 1 visually encapsulates this eye-opening correlation, illustrating a scatterplot that resembles a lightning bolt leaping from the Alaskan legislative chambers to the stock market floor. It's a sight that will surely make anyone's hair stand on end – a bit like those energetic electrons buzzing in a Tesla coil!
The p-value of less than 0.01 further cements the significance of this relationship, strengthening the argument that the number of legislators in Alaska can indeed have a jolting impact on TSLA stock prices. This discovery highlights a fascinating marriage of state-level policy decisions and market reactions, proving that the legislative process is not just bureaucracy – it's a current that can surge through the stock market, potentially electrifying investor sentiment.
Our results support the notion that shifts in the Alaskan legislative landscape may not just shape state policy but also send shockwaves through the financial markets, specifically the value of TSLA stock. It seems that the decisions made in the Last Frontier can radiate far beyond its borders, affecting the value of a company that's revolutionizing transportation as we know it. It's as though the legislation and stock prices are engaged in a dance as wild and unpredictable as an Alaskan winter storm – showing that the markets are not only influenced by global economic forces but also by local political events.
In conclusion, our findings suggest that while individuals may be polar bears apart in their thinking about state policy and the stock market, the data reveals a current of connection that could rival the mighty rivers of the 49th state. The implications of our research extend as far and wide as the sprawling Alaskan landscape, demonstrating the potential for state-level decisions to unleash a force as powerful as the Northern Lights on the stock market stage.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

As we wrap up our exploration of the mysterious link between Alaskan legislators and TSLA stock, we can't help but marvel at the shocking connection we've unearthed. It's as if the Northern Lights have decided to have a dance-off with market trends, and boy, is it a spectacle to behold! The correlation coefficient of 0.9711894 is so strong, it makes one wonder if the Alaskan legislature has been secretly moonlighting as market influencers.

But fear not, fellow researchers, for we have shed light on this electrifying relationship, and we can confidently say that this isn't just some spark of coincidence. With a p-value of less than 0.01, the significance of our findings is as clear as the midnight sun in the Arctic sky. It's as if the very fabric of the market is woven with the legislative decisions made in the Land of the Midnight Sun.

So, is this correlation just a fluke, like a salmon out of water? Absolutely not! Our results are as solid as an Alaskan iceberg, indicating a real and impactful connection between legislative activity and TSLA stock price fluctuations. It's like the market has been taking cues from the legislative playbook, making one wonder if there's a secret stock market ticker tape running through the legislative chambers in Juneau.

In conclusion, our findings suggest that the number of legislators in Alaska indeed has a shocking influence on TSLA stock prices. This groundbreaking discovery is as unexpected as stumbling upon a grizzly bear during a leisurely hike. Therefore, there's no need for further research in this area. We can confidently say that the Last Frontier's legislative current has woven its way into the current market, and any further study might just be as unnecessary as bringing snow to an Alaskan winter!


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 Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Legislative Lightning


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 Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Alaskan Legislation and Financial Analysis


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 Legislate to Elevate: Analyzing the Legislators in Alaska and the Electrifying Effects on Tesla's Stock Price with an abstract of This research paper delves into the surprising connection between the number of legislators in Alaska and the fluctuating stock price of Tesla (TSLA) over the past decade. With a diligent examination of data sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) from 2011 to 2021, our team uncovered a striking correlation coefficient of 0.9711894 and a p-value less than 0.01. Despite the seemingly incongruous nature of these two variables, our findings shed light on how legislative developments in Alaska may have had a shockingly electrifying impact on TSLA stock. So, let's strap in for an exhilarating journey through the peaks and valleys of Alaskan lawmakers and Tesla's stock prices, as we uncover the legislative current that appears to resonate with the market currents.

ChatGPT:

legislators Alaska, Tesla stock price, correlation coefficient, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, TSLA stock, legislative developments, Alaskan lawmakers, market currents, stock price fluctuations

*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 legislators in Alaska
Detailed data title: BLS estimate of legislators in Alaska
Source: Bureau of Larbor Statistics
See what else correlates with The number of legislators in Alaska

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

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

Correlation r = 0.9711894 (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 = 0.9432089 (Coefficient of determination)
This means 94.3% of the change in the one variable (i.e., Tesla's stock price (TSLA)) is predictable based on the change in the other (i.e., The number of legislators in Alaska) over the 11 years from 2011 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.6E-7. 0.0000006567299646462054000000
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 6.6E-5% of random cases. Said differently, if you correlated 1,522,696 random variables You don't actually need 1 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 10 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 10 because we have two variables measured over a period of 11 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.

[ 0.89, 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.
20112012201320142015201620172018201920202021
The number of legislators in Alaska (Legislators)100170160150100110120180200180590
Tesla's stock price (TSLA) (Stock price)1.791.932.339.9914.8615.3814.3220.820.4128.3239.82




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([100,170,160,150,100,110,120,180,200,180,590,])
array_2 = np.array([1.79,1.93,2.33,9.99,14.86,15.38,14.32,20.8,20.41,28.3,239.82,])
array_1_name = "The number of legislators in Alaska"
array_2_name = "Tesla's stock price (TSLA)"

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

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



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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: 1732 · Black Variable ID: 4679 · Red Variable ID: 1545
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