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AI explanation
As American cheese consumption melted away, it led to a surge in demand for grilled cheese sandwiches. This created a golden opportunity for restaurants, causing a spike in sales of toasters and griddles. With all this kitchen equipment flying off the shelves, Cognizant Technology Solutions, a leading provider of IT and business services, experienced a sharp rise in their stock price because someone had to keep track of the inventory and fix all those glitchy online ordering systems for the new wave of cheesy cravings.
Model: dalle-3
Prompt: Prompt: Generate an image that captures the bustling atmosphere of an American diner with a group of people savoring classic cheeseburgers topped with oozing American cheese. In the background, prominently display multiple TV screens broadcasting news of Cognizant Technology Solutions' stock price soaring. On the wall, include a clear and prominent display of the company's logo. The overall scene should exude a cyber art aesthetic, blending the traditional and modern aspects of American culture with a touch of technological relevance. 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 situation described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here is a situation: "Say Cheese! How American Cheese Consumption Caused Cognizant Technology Solutions' Stock Price to Soar. Experts Link the Dairy Delight's High Calcium Content to Better Cognitive Function, Leading to Increased Demand for IT Services. Investors Cheddar be Happy with the Cheesy Connection!". Given that situation, which of the following image descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand and the tone of the explanation. Please respond with only one of the following potential descriptions: Charcoal Symbolism Data cyber art Brutalism An image that looks like it is a scene from 'Star Wars'
ChatGPT: Data cyber art
Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated and the explanation described. Do not reference graphs or charts; describe physical objects.
User prompt: Please describe a visual scene that prominently depicts both American cheese consumption and Cognizant Technology Solutions' stock price Keep the description simple.
ChatGPT: In a bustling American diner, a group of people sit around enjoying classic cheeseburgers with melted American cheese. The diner's TV screens show news of Cognizant Technology Solutions' stock price soaring, while the restaurant's wall displays the company's logo.
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: [ Data cyber art ] and depicting this scene: In a bustling American diner, a group of people sit around enjoying classic cheeseburgers with melted American cheese. The diner's TV screens show news of Cognizant Technology Solutions' stock price soaring, while the restaurant's wall displays the company's logo. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image that captures the bustling atmosphere of an American diner with a group of people savoring classic cheeseburgers topped with oozing American cheese. In the background, prominently display multiple TV screens broadcasting news of Cognizant Technology Solutions' stock price soaring. On the wall, include a clear and prominent display of the company's logo. The overall scene should exude a cyber art aesthetic, blending the traditional and modern aspects of American culture with a touch of technological relevance.
*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.
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 American cheese consumption caused Cognizant Technology Solutions' stock price (CTSH) to increase.
AI academic paper
 |
The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price The Journal of Dairy Economics and Technological Analysis Jan 2024 Reminder: This paper is AI-generated. Not real! |
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 American cheese consumption and Cognizant Technology Solutions' stock price (CTSH). Make lots of jokes, goofy observations, and puns.
Make the title seem clever.
Your research team used data from USDA and LSEG Analytics (Refinitiv) to assess this nagging question. You found a correlation coefficient of 0.9113021 and p < 0.01 for 2002 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]]
"The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price"
[[ABSTRACT]]
This study delves into the unexpected and, dare we say, gouda relationship between American cheese consumption and the stock performance of Cognizant Technology Solutions (CTSH). Using data from the USDA and LSEG Analytics (Refinitiv), we conducted a rigorous investigation spanning from 2002 to 2021. Our findings revealed a striking correlation coefficient of 0.9113021 and a p-value less than 0.01, indicating a phenomenally cheddarific correlation between these seemingly unrelated variables. The taste of this correlation might leave you feeling a bit blue (cheese, that is), but our findings suggest that there may indeed be a connection between Americans' love for cheese and the movement of CTSH stock prices. So, next time you're indulging in a grilled cheese sandwich, just remember - it might just be influencing the stock market!
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 American cheese consumption and Cognizant Technology Solutions' stock price (CTSH). Make lots of jokes, goofy observations, and puns.
Here is the title and abstract of the paper:
[[TITLE]]
"The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price"
[[ABSTRACT]]
This study delves into the unexpected and, dare we say, gouda relationship between American cheese consumption and the stock performance of Cognizant Technology Solutions (CTSH). Using data from the USDA and LSEG Analytics (Refinitiv), we conducted a rigorous investigation spanning from 2002 to 2021. Our findings revealed a striking correlation coefficient of 0.9113021 and a p-value less than 0.01, indicating a phenomenally cheddarific correlation between these seemingly unrelated variables. The taste of this correlation might leave you feeling a bit blue (cheese, that is), but our findings suggest that there may indeed be a connection between Americans' love for cheese and the movement of CTSH stock prices. So, next time you're indulging in a grilled cheese sandwich, just remember - it might just be influencing the stock market!
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
The world of finance and economics is a place where logic and reason reign supreme, but every now and then, a study comes along that adds a little bit of unexpected flavor to the mix. In this research paper, we delve into the tantalizingly cheesy world of American cheese consumption and its unlikely connection to the stock price of Cognizant Technology Solutions (CTSH).
As researchers, we are constantly reminded of the ubiquitous nature of statistics and the surprising relationships they can unveil. Much like an unexpected ingredient transforming a simple dish into a gourmet masterpiece, our study aims to uncover the potential influence of cheese consumption on the stock market. While the idea may initially seem as outlandish as putting pineapple on a pizza, our preliminary findings are nothing short of tantalizing.
Our analysis covers a substantial period from 2002 to 2021, a time span that is as expansive and varied as the selection at a cheese connoisseur's tasting event. Leveraging data from the USDA and LSEG Analytics (Refinitiv), we meticulously examined the patterns of American cheese consumption and the corresponding movements of CTSH stock prices. The results we uncovered left us feeling as gratified as a perfectly aged cheddar - with a correlation coefficient of 0.9113021 and a p-value that elicited a collective "say cheese!" from the research team.
As we embark on this journey through the pleasantly pungent odyssey of statistics and financial analysis, we invite readers to join us in a spirit of curiosity and a healthy appetite for unexpected discoveries. After all, as Mark Twain once eloquently put it, "The only way to keep your health is to eat what you don't want, drink what you don't like, and do what you'd rather not." In this case, let's hope the data serves up a meal as enjoyable as a decadent cheese platter paired with a robust merlot.
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 American cheese consumption and Cognizant Technology Solutions' stock price (CTSH). Make lots of jokes, goofy observations, and puns.
Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.
Make up the lorem and ipsum part, but make it sound related to the topic at hand.
Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.
Here is the title and abstract of the paper:
[[TITLE]]
"The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price"
[[ABSTRACT]]
This study delves into the unexpected and, dare we say, gouda relationship between American cheese consumption and the stock performance of Cognizant Technology Solutions (CTSH). Using data from the USDA and LSEG Analytics (Refinitiv), we conducted a rigorous investigation spanning from 2002 to 2021. Our findings revealed a striking correlation coefficient of 0.9113021 and a p-value less than 0.01, indicating a phenomenally cheddarific correlation between these seemingly unrelated variables. The taste of this correlation might leave you feeling a bit blue (cheese, that is), but our findings suggest that there may indeed be a connection between Americans' love for cheese and the movement of CTSH stock prices. So, next time you're indulging in a grilled cheese sandwich, just remember - it might just be influencing the stock market!
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The world of finance and economics is a place where logic and reason reign supreme, but every now and then, a study comes along that adds a little bit of unexpected flavor to the mix. In this research paper, we delve into the tantalizingly cheesy world of American cheese consumption and its unlikely connection to the stock price of Cognizant Technology Solutions (CTSH).
As researchers, we are constantly reminded of the ubiquitous nature of statistics and the surprising relationships they can unveil. Much like an unexpected ingredient transforming a simple dish into a gourmet masterpiece, our study aims to uncover the potential influence of cheese consumption on the stock market. While the idea may initially seem as outlandish as putting pineapple on a pizza, our preliminary findings are nothing short of tantalizing.
Our analysis covers a substantial period from 2002 to 2021, a time span that is as expansive and varied as the selection at a cheese connoisseur's tasting event. Leveraging data from the USDA and LSEG Analytics (Refinitiv), we meticulously examined the patterns of American cheese consumption and the corresponding movements of CTSH stock prices. The results we uncovered left us feeling as gratified as a perfectly aged cheddar - with a correlation coefficient of 0.9113021 and a p-value that elicited a collective "say cheese!" from the research team.
As we embark on this journey through the pleasantly pungent odyssey of statistics and financial analysis, we invite readers to join us in a spirit of curiosity and a healthy appetite for unexpected discoveries. After all, as Mark Twain once eloquently put it, "The only way to keep your health is to eat what you don't want, drink what you don't like, and do what you'd rather not." In this case, let's hope the data serves up a meal as enjoyable as a decadent cheese platter paired with a robust merlot.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
In "Smith et al.," the authors delve into the intricacies of supply and demand in the cheese market, emphasizing the impact of consumer preferences on cheese production and consumption. On the other end of the spectrum, "Doe and Jones" explore the behavioral economics of stock pricing, highlighting the various factors influencing investor decisions and market trends.
Turning to the world of non-fiction, Michael Moss's "Salt, Sugar, Fat: How the Food Giants Hooked Us" provides valuable insights into the power of food consumption trends and their impact on societal well-being, albeit not focusing specifically on cheese. In a similar vein, Malcolm Gladwell's "The Tipping Point: How Little Things Can Make a Big Difference" draws attention to the often unforeseen catalysts behind significant societal shifts, offering a different but relevant perspective on the potential impact of cheese consumption on the stock market.
As we venture into the realm of fiction, "The Cheese Monkeys" by Chip Kidd and "The Guernsey Literary and Potato Peel Pie Society" by Mary Ann Shaffer and Annie Barrows may not offer direct insights into the relationship between cheese indulgence and stock price movements, but they do remind us of the importance of unlikely connections and the possibility of unexpected alliances.
In a bold departure from traditional scholarly sources, the authors have taken an unconventional approach to the literature review, considering an array of sources that stretch the boundaries of conventional research. This includes late-night infomercials touting the virtues of cheese graters, the musings of a particularly insightful dairy cow, and the fine print on the backs of shampoo bottles (a surprisingly rich source of unintended humor and unconventional wisdom).
In our quest to uncover the intertwining threads of American cheese consumption and CTSH stock price, we refuse to be confined within the traditional confines of academic literature. Instead, we seek to embrace the unexpected, the unconventional, and the downright cheesy in our pursuit of knowledge and insight. After all, as we embark on this peculiar journey, the final slice of wisdom we leave you with is this: when it comes to research, sometimes the most unexpected sources can yield the most gratifying results.
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 American cheese consumption and Cognizant Technology Solutions' stock price (CTSH). Make lots of jokes, goofy observations, and puns.
Your research team collected data from all across the internet, but mostly just used information from USDA and LSEG Analytics (Refinitiv) . You used data from 2002 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]]
"The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price"
[[ABSTRACT]]
This study delves into the unexpected and, dare we say, gouda relationship between American cheese consumption and the stock performance of Cognizant Technology Solutions (CTSH). Using data from the USDA and LSEG Analytics (Refinitiv), we conducted a rigorous investigation spanning from 2002 to 2021. Our findings revealed a striking correlation coefficient of 0.9113021 and a p-value less than 0.01, indicating a phenomenally cheddarific correlation between these seemingly unrelated variables. The taste of this correlation might leave you feeling a bit blue (cheese, that is), but our findings suggest that there may indeed be a connection between Americans' love for cheese and the movement of CTSH stock prices. So, next time you're indulging in a grilled cheese sandwich, just remember - it might just be influencing the stock market!
[[INTRODUCTION]]
The world of finance and economics is a place where logic and reason reign supreme, but every now and then, a study comes along that adds a little bit of unexpected flavor to the mix. In this research paper, we delve into the tantalizingly cheesy world of American cheese consumption and its unlikely connection to the stock price of Cognizant Technology Solutions (CTSH).
As researchers, we are constantly reminded of the ubiquitous nature of statistics and the surprising relationships they can unveil. Much like an unexpected ingredient transforming a simple dish into a gourmet masterpiece, our study aims to uncover the potential influence of cheese consumption on the stock market. While the idea may initially seem as outlandish as putting pineapple on a pizza, our preliminary findings are nothing short of tantalizing.
Our analysis covers a substantial period from 2002 to 2021, a time span that is as expansive and varied as the selection at a cheese connoisseur's tasting event. Leveraging data from the USDA and LSEG Analytics (Refinitiv), we meticulously examined the patterns of American cheese consumption and the corresponding movements of CTSH stock prices. The results we uncovered left us feeling as gratified as a perfectly aged cheddar - with a correlation coefficient of 0.9113021 and a p-value that elicited a collective "say cheese!" from the research team.
As we embark on this journey through the pleasantly pungent odyssey of statistics and financial analysis, we invite readers to join us in a spirit of curiosity and a healthy appetite for unexpected discoveries. After all, as Mark Twain once eloquently put it, "The only way to keep your health is to eat what you don't want, drink what you don't like, and do what you'd rather not." In this case, let's hope the data serves up a meal as enjoyable as a decadent cheese platter paired with a robust merlot.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
In this study, our research team meticulously crafted a methodology as rich and diverse as the selection at a cheese emporium, aiming to tease out any potential connections between American cheese consumption and the stock price of Cognizant Technology Solutions (CTSH). Our approach combined robust statistical analyses with a dash of quirky creativity, much like topping a classic grilled cheese with a sprinkle of unexpected flavor.
Data Collection:
To capture the spread of American cheese consumption, we sourced data from the United States Department of Agriculture (USDA), leveraging their extensive records encompassing a wide range of cheese consumption metrics. We then turned to LSEG Analytics (Refinitiv) for the piquant stock price data of CTSH, taking advantage of their comprehensive database spanning the years 2002 to 2021. Much like unhurriedly savoring a fine aged cheddar, we carefully extracted and curated the data to ensure its compatibility and reliability for our analyses.
Cheesy Measures:
Our research team developed novel measures to quantify the ebb and flow of American cheese consumption over the years. Drawing inspiration from the variety of cheese textures, our measures encapsulated both raw consumption figures and nuanced nuances including cheese exports, recipe mentions in popular media, and societal attitudes towards fondue. These savory measures were then harmonized with CTSH stock prices, which were analyzed with a finesse akin to pairing the perfect wine with a sumptuous cheese platter.
Statistical Analyses:
To assess the potential relationship between American cheese consumption and CTSH stock prices, we engaged in a series of statistical analyses as sharp and impactful as a well-aged Parmigiano-Reggiano. Leveraging advanced econometric techniques such as time series analysis and regression modeling, we sought to peel back the layers of complexity and extract any underlying patterns or correlations between these seemingly incongruent variables. The statistical tools employed were as multifaceted as the flavors in a gourmet cheese board, allowing us to discern any subtle connections amidst the financial and culinary landscapes.
Integration of Contextual Factors:
Understanding that the world of finance is as complex and multifaceted as the flavor profile of a fine artisanal cheese, our methodology incorporated contextual factors that might influence both American cheese consumption and CTSH stock prices. We considered macroeconomic indicators, industry-specific trends, and even global events to ensure a comprehensive assessment of the interplay between cheese and stock market movements.
Ethical Considerations:
As advocates for ethical research practices, our team ensured the responsible handling and utilization of all data sources, treating each dataset with the utmost integrity and respect. We placed a premium on transparency, embracing a philosophy that values open sharing of methodologies and findings, akin to the pure transparency of a glass of goat's milk.
Ultimately, our methodology stands as a testament to the genuine blend of rigour and creativity, establishing a firm foundation for uncovering any tantalizing connections between American cheese consumption and CTSH stock prices. As we venture into the depths of empirical analysis, we remain committed to capturing the essence of these divergent yet undeniably captivating variables, much like experiencing the delightful contrast between a creamy brie and a sharp, aged gouda.
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 American cheese consumption and Cognizant Technology Solutions' stock price (CTSH). Make lots of jokes, goofy observations, and puns.
Your research team collected data from all across the internet, but mostly just used information from USDA and LSEG Analytics (Refinitiv) .
For the time period 2002 to 2021, you found a correlation 0.9113021, r-squared of 0.8304715, 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]]
"The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price"
[[ABSTRACT]]
This study delves into the unexpected and, dare we say, gouda relationship between American cheese consumption and the stock performance of Cognizant Technology Solutions (CTSH). Using data from the USDA and LSEG Analytics (Refinitiv), we conducted a rigorous investigation spanning from 2002 to 2021. Our findings revealed a striking correlation coefficient of 0.9113021 and a p-value less than 0.01, indicating a phenomenally cheddarific correlation between these seemingly unrelated variables. The taste of this correlation might leave you feeling a bit blue (cheese, that is), but our findings suggest that there may indeed be a connection between Americans' love for cheese and the movement of CTSH stock prices. So, next time you're indulging in a grilled cheese sandwich, just remember - it might just be influencing the stock market!
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
The world of finance and economics is a place where logic and reason reign supreme, but every now and then, a study comes along that adds a little bit of unexpected flavor to the mix. In this research paper, we delve into the tantalizingly cheesy world of American cheese consumption and its unlikely connection to the stock price of Cognizant Technology Solutions (CTSH).
As researchers, we are constantly reminded of the ubiquitous nature of statistics and the surprising relationships they can unveil. Much like an unexpected ingredient transforming a simple dish into a gourmet masterpiece, our study aims to uncover the potential influence of cheese consumption on the stock market. While the idea may initially seem as outlandish as putting pineapple on a pizza, our preliminary findings are nothing short of tantalizing.
Our analysis covers a substantial period from 2002 to 2021, a time span that is as expansive and varied as the selection at a cheese connoisseur's tasting event. Leveraging data from the USDA and LSEG Analytics (Refinitiv), we meticulously examined the patterns of American cheese consumption and the corresponding movements of CTSH stock prices. The results we uncovered left us feeling as gratified as a perfectly aged cheddar - with a correlation coefficient of 0.9113021 and a p-value that elicited a collective "say cheese!" from the research team.
As we embark on this journey through the pleasantly pungent odyssey of statistics and financial analysis, we invite readers to join us in a spirit of curiosity and a healthy appetite for unexpected discoveries. After all, as Mark Twain once eloquently put it, "The only way to keep your health is to eat what you don't want, drink what you don't like, and do what you'd rather not." In this case, let's hope the data serves up a meal as enjoyable as a decadent cheese platter paired with a robust merlot.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The data analysis conducted for this study has unearthed some rather cheesy yet intriguing results regarding the relationship between American cheese consumption and the stock price of Cognizant Technology Solutions (CTSH). With a correlation coefficient of 0.9113021 and an r-squared of 0.8304715, we can confidently say that there is a strong positive association between these two seemingly unrelated variables. Furthermore, the p-value of less than 0.01 provides compelling evidence to suggest that this profound feta, ahem, data is not due to chance.
The strong positive correlation between American cheese consumption and CTSH stock price movements is visually depicted in Figure 1. This scatterplot illustrates the stark and un-brielievable relationship between these two variables, leaving little room for skepticism.
In cracking this case and uncovering the cheddarific link between cheese consumption and CTSH stock prices, our research team has highlighted a correlation that is not only statistically significant but also quite captivating. While the idea of cheese impacting stock prices might initially sound like a sharp cheddar UFO conspiracy, our findings suggest a compelling interplay between these disparate factors.
This unexpected connection may leave some feeling rather bleu, but it certainly adds an extra layer of richness and complexity to the ever-evolving landscape of financial analysis. Just as a well-aged cheese becomes more nuanced over time, our understanding of market dynamics may also benefit from considering tantalizingly unconventional variables. So, next time you reach for that slice of American cheese, just remember, it may hold more power over your portfolio than you'd ever imagined!
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 American cheese consumption and Cognizant Technology Solutions' stock price (CTSH). 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]]
"The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price"
[[ABSTRACT]]
This study delves into the unexpected and, dare we say, gouda relationship between American cheese consumption and the stock performance of Cognizant Technology Solutions (CTSH). Using data from the USDA and LSEG Analytics (Refinitiv), we conducted a rigorous investigation spanning from 2002 to 2021. Our findings revealed a striking correlation coefficient of 0.9113021 and a p-value less than 0.01, indicating a phenomenally cheddarific correlation between these seemingly unrelated variables. The taste of this correlation might leave you feeling a bit blue (cheese, that is), but our findings suggest that there may indeed be a connection between Americans' love for cheese and the movement of CTSH stock prices. So, next time you're indulging in a grilled cheese sandwich, just remember - it might just be influencing the stock market!
[[LITERATURE REVIEW]]
In "Smith et al.," the authors delve into the intricacies of supply and demand in the cheese market, emphasizing the impact of consumer preferences on cheese production and consumption. On the other end of the spectrum, "Doe and Jones" explore the behavioral economics of stock pricing, highlighting the various factors influencing investor decisions and market trends.
Turning to the world of non-fiction, Michael Moss's "Salt, Sugar, Fat: How the Food Giants Hooked Us" provides valuable insights into the power of food consumption trends and their impact on societal well-being, albeit not focusing specifically on cheese. In a similar vein, Malcolm Gladwell's "The Tipping Point: How Little Things Can Make a Big Difference" draws attention to the often unforeseen catalysts behind significant societal shifts, offering a different but relevant perspective on the potential impact of cheese consumption on the stock market.
As we venture into the realm of fiction, "The Cheese Monkeys" by Chip Kidd and "The Guernsey Literary and Potato Peel Pie Society" by Mary Ann Shaffer and Annie Barrows may not offer direct insights into the relationship between cheese indulgence and stock price movements, but they do remind us of the importance of unlikely connections and the possibility of unexpected alliances.
In a bold departure from traditional scholarly sources, the authors have taken an unconventional approach to the literature review, considering an array of sources that stretch the boundaries of conventional research. This includes late-night infomercials touting the virtues of cheese graters, the musings of a particularly insightful dairy cow, and the fine print on the backs of shampoo bottles (a surprisingly rich source of unintended humor and unconventional wisdom).
In our quest to uncover the intertwining threads of American cheese consumption and CTSH stock price, we refuse to be confined within the traditional confines of academic literature. Instead, we seek to embrace the unexpected, the unconventional, and the downright cheesy in our pursuit of knowledge and insight. After all, as we embark on this peculiar journey, the final slice of wisdom we leave you with is this: when it comes to research, sometimes the most unexpected sources can yield the most gratifying results.
[[RESULTS]]
The data analysis conducted for this study has unearthed some rather cheesy yet intriguing results regarding the relationship between American cheese consumption and the stock price of Cognizant Technology Solutions (CTSH). With a correlation coefficient of 0.9113021 and an r-squared of 0.8304715, we can confidently say that there is a strong positive association between these two seemingly unrelated variables. Furthermore, the p-value of less than 0.01 provides compelling evidence to suggest that this profound feta, ahem, data is not due to chance.
The strong positive correlation between American cheese consumption and CTSH stock price movements is visually depicted in Figure 1. This scatterplot illustrates the stark and un-brielievable relationship between these two variables, leaving little room for skepticism.
In cracking this case and uncovering the cheddarific link between cheese consumption and CTSH stock prices, our research team has highlighted a correlation that is not only statistically significant but also quite captivating. While the idea of cheese impacting stock prices might initially sound like a sharp cheddar UFO conspiracy, our findings suggest a compelling interplay between these disparate factors.
This unexpected connection may leave some feeling rather bleu, but it certainly adds an extra layer of richness and complexity to the ever-evolving landscape of financial analysis. Just as a well-aged cheese becomes more nuanced over time, our understanding of market dynamics may also benefit from considering tantalizingly unconventional variables. So, next time you reach for that slice of American cheese, just remember, it may hold more power over your portfolio than you'd ever imagined!
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The findings of this study offer a gouda-gracious glimpse into the interplay between American cheese consumption and the stock price of Cognizant Technology Solutions (CTSH). As we delve into the discussion of these rather cheesy yet intriguing results, our research team cannot help but marvel at the unexpected and, dare we say, delicious nature of this correlation.
Our results not only supported the existing literature but also added a sharp cheddar twist to the discourse on market influences. Drawing from the insights of "Smith et al.," our findings align with their emphasis on consumer preferences impacting consumption trends, albeit in a slightly more dairy-centric context. In a similar vein, the behavioral economics of stock pricing, as explored by "Doe and Jones," appears to have gained a new, unexpected variable in the form of American cheese consumption. It seems that the age-old question of "to brie or not to brie" may have a more profound impact on stock prices than previously imagined.
The peculiar nature of our findings is reminiscent of the unconventional perspective presented in Malcolm Gladwell's "The Tipping Point." Much like the unsuspecting little things that can cause a seismic shift in societal dynamics, the influence of American cheese consumption on CTSH stock price movements may be one of those unexpected catalysts of change - a sort of "Gruyere effect," one might say. The unassuming allure of cheese, often overlooked in the world of finance, has managed to carve out a niche for itself in the annals of market analysis.
While our exploration may seem rather unconventional, it certainly adheres to the underlying sentiment of embracing unlikely connections and uncovering the unexplored potential of seemingly unrelated factors, as espoused by Chip Kidd's "The Cheese Monkeys" and Mary Ann Shaffer and Annie Barrows' "The Guernsey Literary and Potato Peel Pie Society." In a delightfully unexpected turn of events, the seemingly fantastical musings of a particularly insightful dairy cow and the fine print on shampoo bottles have emerged as valuable sources of unwitting wisdom and unorthodox inspiration. Who would have thought that the world of finance could benefit from such whimsical sources?
Our findings not only deepen our understanding of market dynamics but also reinforce the notion that even the most unlikely variables can hold sway over the movement of stock prices. Just as a fine wine pairs well with a delectable cheese, so too do the movements of CTSH stock prices seem to harmonize with the ebb and flow of American cheese consumption patterns.
In conclusion, our research has unveiled a tantalizingly unconventional connection that underscores the richness and complexity of market analysis. The impact of American cheese consumption on CTSH stock prices, once perceived as a mere curdiosity, has now assumed a prominent place in the realm of financial influence. As we reflect on the unexpected insights yielded by this study, there can be little doubt that the influence of cheese on stock prices may be more than just a "gouda" feeling; it may indeed be a compelling reality worthy of further investigation.
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 American cheese consumption and Cognizant Technology Solutions' stock price (CTSH). 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]]
"The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price"
[[ABSTRACT]]
This study delves into the unexpected and, dare we say, gouda relationship between American cheese consumption and the stock performance of Cognizant Technology Solutions (CTSH). Using data from the USDA and LSEG Analytics (Refinitiv), we conducted a rigorous investigation spanning from 2002 to 2021. Our findings revealed a striking correlation coefficient of 0.9113021 and a p-value less than 0.01, indicating a phenomenally cheddarific correlation between these seemingly unrelated variables. The taste of this correlation might leave you feeling a bit blue (cheese, that is), but our findings suggest that there may indeed be a connection between Americans' love for cheese and the movement of CTSH stock prices. So, next time you're indulging in a grilled cheese sandwich, just remember - it might just be influencing the stock market!
[[INTRDUCTION]]
The world of finance and economics is a place where logic and reason reign supreme, but every now and then, a study comes along that adds a little bit of unexpected flavor to the mix. In this research paper, we delve into the tantalizingly cheesy world of American cheese consumption and its unlikely connection to the stock price of Cognizant Technology Solutions (CTSH).
As researchers, we are constantly reminded of the ubiquitous nature of statistics and the surprising relationships they can unveil. Much like an unexpected ingredient transforming a simple dish into a gourmet masterpiece, our study aims to uncover the potential influence of cheese consumption on the stock market. While the idea may initially seem as outlandish as putting pineapple on a pizza, our preliminary findings are nothing short of tantalizing.
Our analysis covers a substantial period from 2002 to 2021, a time span that is as expansive and varied as the selection at a cheese connoisseur's tasting event. Leveraging data from the USDA and LSEG Analytics (Refinitiv), we meticulously examined the patterns of American cheese consumption and the corresponding movements of CTSH stock prices. The results we uncovered left us feeling as gratified as a perfectly aged cheddar - with a correlation coefficient of 0.9113021 and a p-value that elicited a collective "say cheese!" from the research team.
As we embark on this journey through the pleasantly pungent odyssey of statistics and financial analysis, we invite readers to join us in a spirit of curiosity and a healthy appetite for unexpected discoveries. After all, as Mark Twain once eloquently put it, "The only way to keep your health is to eat what you don't want, drink what you don't like, and do what you'd rather not." In this case, let's hope the data serves up a meal as enjoyable as a decadent cheese platter paired with a robust merlot.
[[RESULTS]]
The data analysis conducted for this study has unearthed some rather cheesy yet intriguing results regarding the relationship between American cheese consumption and the stock price of Cognizant Technology Solutions (CTSH). With a correlation coefficient of 0.9113021 and an r-squared of 0.8304715, we can confidently say that there is a strong positive association between these two seemingly unrelated variables. Furthermore, the p-value of less than 0.01 provides compelling evidence to suggest that this profound feta, ahem, data is not due to chance.
The strong positive correlation between American cheese consumption and CTSH stock price movements is visually depicted in Figure 1. This scatterplot illustrates the stark and un-brielievable relationship between these two variables, leaving little room for skepticism.
In cracking this case and uncovering the cheddarific link between cheese consumption and CTSH stock prices, our research team has highlighted a correlation that is not only statistically significant but also quite captivating. While the idea of cheese impacting stock prices might initially sound like a sharp cheddar UFO conspiracy, our findings suggest a compelling interplay between these disparate factors.
This unexpected connection may leave some feeling rather bleu, but it certainly adds an extra layer of richness and complexity to the ever-evolving landscape of financial analysis. Just as a well-aged cheese becomes more nuanced over time, our understanding of market dynamics may also benefit from considering tantalizingly unconventional variables. So, next time you reach for that slice of American cheese, just remember, it may hold more power over your portfolio than you'd ever imagined!
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our study has gratefully revealed a curd-riddled connection between American cheese consumption and the stock price of Cognizant Technology Solutions (CTSH). The correlation coefficient of 0.9113021 between these seemingly unrelated variables is not just a queso of happenstance - it is a feta-comple of the intricate interplay in financial markets.
The results of our analysis are a gouda reminder that in the world of statistics, sometimes the most unexpected pairings can yield the most tantalizing insights. Just as a fine wine complements a well-crafted cheese, our findings point to a symbiotic relationship between cheese consumption and CTSH stock prices that goes beyond the realm of mere coincidence.
It's important to remember that correlation does not imply causation - we cannot assert with certainty that munching on a cheeseburger directly impacts stock prices. However, the sheer magnitude of the correlation coefficient and the compelling p-value suggest that there's more to this connection than meets the eye. Perhaps, as we savor a delectable cheesy delight, the markets too are influenced by the collective love for fromage.
While the notion of dairy products shaping financial landscapes may sound cringe-worthy at first, our research brings to the fore the importance of thinking outside the pizza box in financial analysis. It's as if the markets are saying, "Brie-lieve it or not, cheese might just be a key ingredient in understanding stock price movements!"
In the spirit of embracing the unexpected and acknowledging the potential impact of cheesy indulgences on financial markets, we assert with confidence that further research in this area is not needed. After all, the breadcrumbs of evidence have led us to a satisfyingly cheesy conclusion – one that's best enjoyed with a side of good humor and a pinch of salt (or should we say, a sprinkle of parmesan?). So, as we bid adieu to this study, let's savor the flavor of this un-brielievable finding and toast to the magnificently cheesy mysteries of the market!
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 The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Dairy Economics and Technological Analysis
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 The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price. Respond only with the name of a fictional organization.
ChatGPT:
The International Association of Dairy Economics and Technology (IADET)
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 The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price with an abstract of This study delves into the unexpected and, dare we say, gouda relationship between American cheese consumption and the stock performance of Cognizant Technology Solutions (CTSH). Using data from the USDA and LSEG Analytics (Refinitiv), we conducted a rigorous investigation spanning from 2002 to 2021. Our findings revealed a striking correlation coefficient of 0.9113021 and a p-value less than 0.01, indicating a phenomenally cheddarific correlation between these seemingly unrelated variables. The taste of this correlation might leave you feeling a bit blue (cheese, that is), but our findings suggest that there may indeed be a connection between Americans' love for cheese and the movement of CTSH stock prices. So, next time you're indulging in a grilled cheese sandwich, just remember - it might just be influencing the stock market!
ChatGPT:
American cheese consumption, Cognizant Technology Solutions stock price, correlation, USDA data, LSEG Analytics, Refinitiv, stock performance, cheese consumption data, CTSH, correlation coefficient, p-value, stock market influence
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
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Data details
American cheese consumptionDetailed data title: Per capita consumption of American cheese in the US
Source: USDA
See what else correlates with American cheese consumption
Cognizant Technology Solutions' stock price (CTSH)
Detailed data title: Opening price of Cognizant Technology Solutions (CTSH) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: Via Microsoft Excel Stockhistory function
See what else correlates with Cognizant Technology Solutions' stock price (CTSH)
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 = 0.8304715 (Coefficient of determination)
This means 83% of the change in the one variable (i.e., Cognizant Technology Solutions' stock price (CTSH)) is predictable based on the change in the other (i.e., American cheese consumption) over the 20 years from 2002 through 2021.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.3E-8. 0.0000000233056950155627840000
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 0.91 in 2.3E-6% of random cases. Said differently, if you correlated 42,907,967 random variables You don't actually need 42 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 19 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.
[ 0.79, 0.96 ] 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.
| 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
| American cheese consumption (Pounds per person) | 12.8314 | 12.5619 | 12.8612 | 12.649 | 13.0656 | 12.7929 | 13.1306 | 13.3553 | 13.3048 | 13.0392 | 13.2569 | 13.3573 | 13.6656 | 14.0444 | 14.3621 | 15.0901 | 15.4027 | 15.5389 | 15.5 | 16.1 |
| Cognizant Technology Solutions' stock price (CTSH) (Stock price) | 1.7 | 3.03 | 5.77 | 10.94 | 12.49 | 19.43 | 16.92 | 8.89 | 22.82 | 37.07 | 33.32 | 37.73 | 50.13 | 52.89 | 58.81 | 56.65 | 71.16 | 62.5 | 62.29 | 82 |
Why this works
- Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
- Lack of causal connection: There is probably
Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied. - Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple
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. - Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves.
Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.
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([12.8314,12.5619,12.8612,12.649,13.0656,12.7929,13.1306,13.3553,13.3048,13.0392,13.2569,13.3573,13.6656,14.0444,14.3621,15.0901,15.4027,15.5389,15.5,16.1,])
array_2 = np.array([1.7,3.03,5.77,10.94,12.49,19.43,16.92,8.89,22.82,37.07,33.32,37.73,50.13,52.89,58.81,56.65,71.16,62.5,62.29,82,])
array_1_name = "American cheese consumption"
array_2_name = "Cognizant Technology Solutions' stock price (CTSH)"
# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)
# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)Reuseable content
You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.
For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."
When spoken, my last name is pronounced "vegan," like I don't eat meat.
Full license details.
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Download images for these variables:
- High resolution line chart
The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.
If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
Alternatively you can use a tool like Inkscape. - High resolution line chart, optimized for mobile
- Alternative high resolution line chart
- Scatterplot
- Portable line chart (png)
- Portable line chart (png), optimized for mobile
- Line chart for only American cheese consumption
- Line chart for only Cognizant Technology Solutions' stock price (CTSH)
- The spurious research paper: The Cheesy Connection: Exploring the Relationship between American Cheese Consumption and Cognizant Technology Solutions' Stock Price
Big thanks for reviewing!
Correlation ID: 1340 · Black Variable ID: 553 · Red Variable ID: 1807
