Spurious correlation #2,283 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Annual US household spending on meats, poultry, fish, and eggs and the second variable is ANSYs' stock price (ANSS). The chart goes from 2002 to 2022, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Annual US household spending on meats, poultry, fish, and eggs and the second variable is ANSYs' stock price (ANSS). The chart goes from 2002 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As household spending on meat, poultry, fish, and eggs increases, more people are consuming protein, leading to a surge in energy. This excess energy is channeled into productivity, causing a spike in demand for software solutions. More specifically, people are unknowingly doing the "chicken dance" while cooking, leading to a higher need for ANSYS software to perfect their dance moves. In reality, the stock price of ANSYS may have no direct connection to household spending on food, but it's always fun to imagine a world where software stocks are influenced by a sudden craving for a meaty barbecue.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate a woodblock painting depicting a family at a bustling grocery store. The family members are each carrying a basket filled with an array of meats, poultry, fish, and eggs. The shelves are abundantly stocked with fresh produce, and the air is filled with the sound of chatter. In the background, a large stock market display shows ANSYs' stock price steadily climbing. The woodblock style should emphasize bold shapes and vibrant colors to capture the lively atmosphere of the grocery store. 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: Annual US household spending on meats, poultry, fish, and eggs and ANSYs' 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: Woodblock painting Macabre Brutalism Voxel art Woodblock painting
ChatGPT: Woodblock painting

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 Annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price together. Keep the description simple.
ChatGPT: In the scene, you see a family at a grocery store, each carrying a basket filled with a variety of meats, poultry, fish, and eggs. The shelves are stocked with fresh produce and the sound of chatter fills the air. In the background, there's a large stock market display with ANSYs' stock price steadily climbing.

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: [ Woodblock painting ] and depicting this scene: In the scene, you see a family at a grocery store, each carrying a basket filled with a variety of meats, poultry, fish, and eggs. The shelves are stocked with fresh produce and the sound of chatter fills the air. In the background, there's a large stock market display with ANSYs' stock price steadily climbing. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate a woodblock painting depicting a family at a bustling grocery store. The family members are each carrying a basket filled with an array of meats, poultry, fish, and eggs. The shelves are abundantly stocked with fresh produce, and the air is filled with the sound of chatter. In the background, a large stock market display shows ANSYs' stock price steadily climbing. The woodblock style should emphasize bold shapes and vibrant colors to capture the lively atmosphere of the grocery store.

*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.
^{Show prompt for explanation} 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 Annual US household spending on meats, poultry, fish, and eggs caused ANSYs' stock price (ANSS) to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price

The Journal of Culinary Economics

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} 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 goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price (ANSS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then 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]]
"Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price"

[[ABSTRACT]]
This paper investigates the potential relationship between annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for the period from 2002 to 2022, a correlation coefficient of 0.9588266 and p < 0.01 was found, indicating a strong positive correlation between the two variables. The findings of this study suggest that changes in consumer spending on animal products may have a noticeable impact on the stock performance of ANSYs. It's clear that when it comes to market trends, the stakes are high, but this research suggests that poultry is no chicken when it comes to influencing stock prices.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The consumption habits and preferences of the American household have long been a topic of interest for economists and market analysts alike. In recent years, the relationship between consumer spending and stock prices has garnered increasing attention, with researchers seeking to uncover potential correlations and causative factors. Our study contributes to this burgeoning field by examining the connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs, a leading player in the animal protein industry. We aim to shed light on whether the financial performance of ANSYs is correlated with the carnivorous inclinations of the American populace.
In the pursuit of this investigation, we have employed a robust dataset spanning the period from 2002 to 2022, sourced from the venerable Bureau of Labor Statistics and LSEG Analytics (Refinitiv). Our analysis revealed a striking correlation coefficient of 0.9588266 and p < 0.01, signifying a robust positive correlation between annual household expenditures on animal-derived sustenance and the stock price of ANSYs. The results of our study suggest that fluctuations in consumer spending on meat, poultry, fish, and eggs may exert a discernible influence on the performance of ANSYs in the stock market. It appears that when it comes to the financial realm, the cluck stops here for poultry products.
As we delve into the nuances of this correlation, it is essential to recognize the multifaceted factors that interplay within the domain of consumer spending and stock performance. While correlation does not imply causation, the empirical evidence presented in this study invites further scrutiny into the potential mechanisms through which dietary habits intersect with investment behaviors. The implications of these findings extend beyond mere market analysis, delving into the underlying societal patterns and economic forces that shape the contemporary landscape of commerce.
Our endeavor is underpinned by the conviction that seemingly disparate domains—culinary predilections and stock market dynamics—can intertwine in unforeseen ways, yielding insights that transcend conventional boundaries. Through unearthing these connections, we aim to carve a niche for interdisciplinary inquiry, where the seemingly esoteric realm of consumer behavior intertwines with the rigor of financial analysis. As we embark on this scholarly quest, we invite the reader to join us in uncovering the meaty fabric of economic interactions, recognizing that in the realm of financial analysis, there's always more than one way to slice the data.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find that household consumption patterns play a crucial role in influencing stock prices, with particular emphasis on the meat and poultry sector. Similarly, "Doe and Johnson" suggest that consumer spending on animal-derived food products exhibits a substantial correlation with the performance of related stocks in the market. Furthermore, "Jones and Smith" underscore the impact of dietary preferences on investment behaviors, highlighting the potential repercussions of shifting meat consumption patterns on stock valuation.

Turning to broader literature, "The Omnivore's Dilemma" by Michael Pollan and "In Defense of Food" by Michael Pollan delve into the complexities of food choices, including the consumption of meats, poultry, fish, and eggs, and their broader societal implications. In the realm of fiction, novels such as "A Tale of Two Chickens" and "The Fisherman's Catch" may not directly address stock prices, but they offer a whimsical yet insightful exploration of the human-animal food dynamic.

Expanding our search into more unconventional sources, the back of shampoo bottles surprisingly yielded little information on the correlation between consumer spending on meats, poultry, fish, and eggs and stock prices. While the lather and rinse were refreshing, the financial insights were unfortunately lacking.

As we navigate through the labyrinth of literature, it becomes evident that the intersection of dietary habits and stock market performance is not just a meat-and-potatoes issue; rather, it's a complex and multifaceted phenomenon that leaves no stone unturned. The literature, much like a well-seasoned steak, is rich and diverse, offering a plethora of perspectives on the tantalizing relationship between what's on our plates and what's on the stock exchange.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price (ANSS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Labor Statistics and LSEG Analytics (Refinitiv) . You used data from 2002 to 2022

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price"

[[ABSTRACT]]
This paper investigates the potential relationship between annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for the period from 2002 to 2022, a correlation coefficient of 0.9588266 and p < 0.01 was found, indicating a strong positive correlation between the two variables. The findings of this study suggest that changes in consumer spending on animal products may have a noticeable impact on the stock performance of ANSYs. It's clear that when it comes to market trends, the stakes are high, but this research suggests that poultry is no chicken when it comes to influencing stock prices.

[[INTRODUCTION]]
The consumption habits and preferences of the American household have long been a topic of interest for economists and market analysts alike. In recent years, the relationship between consumer spending and stock prices has garnered increasing attention, with researchers seeking to uncover potential correlations and causative factors. Our study contributes to this burgeoning field by examining the connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs, a leading player in the animal protein industry. We aim to shed light on whether the financial performance of ANSYs is correlated with the carnivorous inclinations of the American populace.
In the pursuit of this investigation, we have employed a robust dataset spanning the period from 2002 to 2022, sourced from the venerable Bureau of Labor Statistics and LSEG Analytics (Refinitiv). Our analysis revealed a striking correlation coefficient of 0.9588266 and p < 0.01, signifying a robust positive correlation between annual household expenditures on animal-derived sustenance and the stock price of ANSYs. The results of our study suggest that fluctuations in consumer spending on meat, poultry, fish, and eggs may exert a discernible influence on the performance of ANSYs in the stock market. It appears that when it comes to the financial realm, the cluck stops here for poultry products.
As we delve into the nuances of this correlation, it is essential to recognize the multifaceted factors that interplay within the domain of consumer spending and stock performance. While correlation does not imply causation, the empirical evidence presented in this study invites further scrutiny into the potential mechanisms through which dietary habits intersect with investment behaviors. The implications of these findings extend beyond mere market analysis, delving into the underlying societal patterns and economic forces that shape the contemporary landscape of commerce.
Our endeavor is underpinned by the conviction that seemingly disparate domains—culinary predilections and stock market dynamics—can intertwine in unforeseen ways, yielding insights that transcend conventional boundaries. Through unearthing these connections, we aim to carve a niche for interdisciplinary inquiry, where the seemingly esoteric realm of consumer behavior intertwines with the rigor of financial analysis. As we embark on this scholarly quest, we invite the reader to join us in uncovering the meaty fabric of economic interactions, recognizing that in the realm of financial analysis, there's always more than one way to slice the data.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the tantalizing tango between the American love affair with animal-derived sustenance and the gyrations of the stock market, we employed a methodological medley that would make even the most seasoned statistician's head spin (albeit in a delightfully academic manner).

First and foremost, we sourced our data from the hallowed halls of the Bureau of Labor Statistics and the labyrinthine archives of LSEG Analytics (Refinitiv), meticulously extracting information on annual US household spending on meats, poultry, fish, and eggs with the zeal of a connoisseur searching for the perfect cut of steak.

Having procured our treasure trove of data, we proceeded to perform a spellbinding display of statistical wizardry. Employing the venerable technique of time-series analysis, we teased out the fluctuations in annual household expenditures on meaty delights from the year 2002 through 2022. This involved dancing a delicate statistical minuet, harmonizing the rhythmic undulations of consumer spending with the staccato beats of the stock prices of ANSYs.

With the finesse of a trapeze artist navigating the nuances of market dynamics, we then unleashed the formidable power of correlation analysis. Armed with Python, R, and a touch of sheer intellectual fortitude, we computed the correlation coefficient and its associated significance level, casting a probing eye over the tantalizing interplay between carnivorous cravings and stock market success.

To ensure the robustness of our findings, we subjected our data to a rigorous battery of sensitivity analyses and robustness checks, scrutinizing our results with the precision of a discerning wine connoisseur sipping the finest vintage. This process fortified the foundations of our statistical edifice, affirming the solidity of the relationship between household spending on animal products and the fluctuations of ANSYs' stock price.

In the spirit of scholarly camaraderie, we acknowledge that our methods may not have been as straightforward as a chicken crossing the road, but we stand by the integrity of our approach. For, in the circus of statistical analysis, sometimes the most elaborate acrobatics are needed to capture the whimsy of economic correlations.

In the following sections, we present the delectable fruits of our labor, laying bare the enticing connection between household spending on meats, poultry, fish, and eggs, and the captivating ebbs and flows of ANSYs' stock price. So, dear reader, ready your intellectual palate for a feast of empirical insights, served with a side of academic mirth.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price (ANSS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Labor Statistics and LSEG Analytics (Refinitiv) .

For the time period 2002 to 2022, you found a correlation 0.9588266, r-squared of 0.9193484, 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]]
"Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price"

[[ABSTRACT]]
This paper investigates the potential relationship between annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for the period from 2002 to 2022, a correlation coefficient of 0.9588266 and p < 0.01 was found, indicating a strong positive correlation between the two variables. The findings of this study suggest that changes in consumer spending on animal products may have a noticeable impact on the stock performance of ANSYs. It's clear that when it comes to market trends, the stakes are high, but this research suggests that poultry is no chicken when it comes to influencing stock prices.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The consumption habits and preferences of the American household have long been a topic of interest for economists and market analysts alike. In recent years, the relationship between consumer spending and stock prices has garnered increasing attention, with researchers seeking to uncover potential correlations and causative factors. Our study contributes to this burgeoning field by examining the connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs, a leading player in the animal protein industry. We aim to shed light on whether the financial performance of ANSYs is correlated with the carnivorous inclinations of the American populace.
In the pursuit of this investigation, we have employed a robust dataset spanning the period from 2002 to 2022, sourced from the venerable Bureau of Labor Statistics and LSEG Analytics (Refinitiv). Our analysis revealed a striking correlation coefficient of 0.9588266 and p < 0.01, signifying a robust positive correlation between annual household expenditures on animal-derived sustenance and the stock price of ANSYs. The results of our study suggest that fluctuations in consumer spending on meat, poultry, fish, and eggs may exert a discernible influence on the performance of ANSYs in the stock market. It appears that when it comes to the financial realm, the cluck stops here for poultry products.
As we delve into the nuances of this correlation, it is essential to recognize the multifaceted factors that interplay within the domain of consumer spending and stock performance. While correlation does not imply causation, the empirical evidence presented in this study invites further scrutiny into the potential mechanisms through which dietary habits intersect with investment behaviors. The implications of these findings extend beyond mere market analysis, delving into the underlying societal patterns and economic forces that shape the contemporary landscape of commerce.
Our endeavor is underpinned by the conviction that seemingly disparate domains—culinary predilections and stock market dynamics—can intertwine in unforeseen ways, yielding insights that transcend conventional boundaries. Through unearthing these connections, we aim to carve a niche for interdisciplinary inquiry, where the seemingly esoteric realm of consumer behavior intertwines with the rigor of financial analysis. As we embark on this scholarly quest, we invite the reader to join us in uncovering the meaty fabric of economic interactions, recognizing that in the realm of financial analysis, there's always more than one way to slice the data.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The statistical analysis of the relationship between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs yielded intriguing results. Over the period from 2002 to 2022, a remarkably high correlation coefficient of 0.9588266 was observed, accompanied by an r-squared value of 0.9193484. Additionally, the p-value was found to be less than 0.01, indicating a statistically significant association between the variables under scrutiny.

As shown in Figure 1, the scatterplot vividly illustrates the conspicuous positive correlation between annual household expenditures on animal-derived sustenance and the stock price of ANSYs. It seems that when it comes to financial analysis, the relationship between consumers' carnivorous tendencies and stock market performance is not simply a matter of "chicken feed."

The substantial correlation coefficient and r-squared value indicate that approximately 91.9% of the variance in ANSYs' stock price can be explained by changes in annual US household spending on meat, poultry, fish, and eggs. These findings imply that the consumption patterns of carnivorous culinary delights play a significant role in shaping the market behavior of ANSYs. When it comes to the stock market, it appears that the meat of the matter lies in consumer preferences for animal-derived protein.

The profound implications of these results prompt further inquiry into the intricate mechanisms through which consumer spending habits intersect with stock market dynamics. Delving into the meaty fabric of economic interactions, this research underlines the interconnectedness of seemingly incongruous realms, demonstrating that financial analysis is not always a cut and dried affair.

In light of these findings, it is evident that the stock performance of ANSYs is intricately intertwined with the ebb and flow of consumer spending on meat, poultry, fish, and eggs. This correlation underscores the significance of animal-derived sustenance not only in culinary contexts but also in the intricate dance of market trends. It goes to show that when it comes to the stock market, the bullish behavior of consumers towards meat and animal products is nothing to snicker at.

It is essential to recognize that while correlation does not imply causation, the robust statistical relationship uncovered in this study opens the door to further investigation into the underlying mechanisms at play. As we chew over these findings, the tantalizing prospect of uncovering the steakholder interests at play in the intersection of culinary indulgences and financial phenomena comes to the fore. With a mix of statistical rigor and a pinch of humor, this study whets the appetite for future research endeavors that aim to unearth the sizzling dynamics of consumer behavior and stock market performance.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price (ANSS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price"

[[ABSTRACT]]
This paper investigates the potential relationship between annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for the period from 2002 to 2022, a correlation coefficient of 0.9588266 and p < 0.01 was found, indicating a strong positive correlation between the two variables. The findings of this study suggest that changes in consumer spending on animal products may have a noticeable impact on the stock performance of ANSYs. It's clear that when it comes to market trends, the stakes are high, but this research suggests that poultry is no chicken when it comes to influencing stock prices.

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find that household consumption patterns play a crucial role in influencing stock prices, with particular emphasis on the meat and poultry sector. Similarly, "Doe and Johnson" suggest that consumer spending on animal-derived food products exhibits a substantial correlation with the performance of related stocks in the market. Furthermore, "Jones and Smith" underscore the impact of dietary preferences on investment behaviors, highlighting the potential repercussions of shifting meat consumption patterns on stock valuation.
Turning to broader literature, "The Omnivore's Dilemma" by Michael Pollan and "In Defense of Food" by Michael Pollan delve into the complexities of food choices, including the consumption of meats, poultry, fish, and eggs, and their broader societal implications. In the realm of fiction, novels such as "A Tale of Two Chickens" and "The Fisherman's Catch" may not directly address stock prices, but they offer a whimsical yet insightful exploration of the human-animal food dynamic.
Expanding our search into more unconventional sources, the back of shampoo bottles surprisingly yielded little information on the correlation between consumer spending on meats, poultry, fish, and eggs and stock prices. While the lather and rinse were refreshing, the financial insights were unfortunately lacking.
As we navigate through the labyrinth of literature, it becomes evident that the intersection of dietary habits and stock market performance is not just a meat-and-potatoes issue; rather, it's a complex and multifaceted phenomenon that leaves no stone unturned. The literature, much like a well-seasoned steak, is rich and diverse, offering a plethora of perspectives on the tantalizing relationship between what's on our plates and what's on the stock exchange.

[[RESULTS]]
The statistical analysis of the relationship between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs yielded intriguing results. Over the period from 2002 to 2022, a remarkably high correlation coefficient of 0.9588266 was observed, accompanied by an r-squared value of 0.9193484. Additionally, the p-value was found to be less than 0.01, indicating a statistically significant association between the variables under scrutiny.
As shown in Figure 1, the scatterplot vividly illustrates the conspicuous positive correlation between annual household expenditures on animal-derived sustenance and the stock price of ANSYs. It seems that when it comes to financial analysis, the relationship between consumers' carnivorous tendencies and stock market performance is not simply a matter of "chicken feed."
The substantial correlation coefficient and r-squared value indicate that approximately 91.9% of the variance in ANSYs' stock price can be explained by changes in annual US household spending on meat, poultry, fish, and eggs. These findings imply that the consumption patterns of carnivorous culinary delights play a significant role in shaping the market behavior of ANSYs. When it comes to the stock market, it appears that the meat of the matter lies in consumer preferences for animal-derived protein.
The profound implications of these results prompt further inquiry into the intricate mechanisms through which consumer spending habits intersect with stock market dynamics. Delving into the meaty fabric of economic interactions, this research underlines the interconnectedness of seemingly incongruous realms, demonstrating that financial analysis is not always a cut and dried affair.
In light of these findings, it is evident that the stock performance of ANSYs is intricately intertwined with the ebb and flow of consumer spending on meat, poultry, fish, and eggs. This correlation underscores the significance of animal-derived sustenance not only in culinary contexts but also in the intricate dance of market trends. It goes to show that when it comes to the stock market, the bullish behavior of consumers towards meat and animal products is nothing to snicker at.
It is essential to recognize that while correlation does not imply causation, the robust statistical relationship uncovered in this study opens the door to further investigation into the underlying mechanisms at play. As we chew over these findings, the tantalizing prospect of uncovering the steakholder interests at play in the intersection of culinary indulgences and financial phenomena comes to the fore. With a mix of statistical rigor and a pinch of humor, this study whets the appetite for future research endeavors that aim to unearth the sizzling dynamics of consumer behavior and stock market performance.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study provide compelling evidence of a strong positive correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs. The results align with prior research by Smith et al., Doe and Johnson, and Jones and Smith, emphasizing the significant influence of consumer consumption patterns on stock market performance. Furthermore, the literature reviewed, including Michael Pollan's works and even whimsical novels like "A Tale of Two Chickens," has shed light on the multifaceted relationship between dietary habits and stock valuation. The absence of correlation on the back of shampoo bottles, while disappointing, reflects the importance of thorough and unconventional literature reviews.

The substantial correlation coefficient and r-squared value obtained in this study confirm the tight link between consumer spending on animal-derived products and ANSYs' stock price. The scatterplot clearly depicts the robust positive correlation, echoing the sentiment that in the realm of financial analysis, the stakes are high, and the impact of carnivorous tendencies on stock performance is no "chicken feed."

We must acknowledge the limitations of our study. While our findings reveal a strong statistical relationship, correlation does not imply causation. However, it's hard to ignore the "meat" of the matter - the significant role of consumer preferences for animal-based protein in shaping market behavior. This calls for further investigation into the underlying mechanisms at play, inviting future research to delve into the sizzling dynamics of consumer behavior and stock market performance.

In light of these results, it's clear that the stock performance of ANSYs is not simply a matter of numbers; it's a reflection of the carnivorous inclinations of consumers. As we navigate this labyrinth of economic interactions, it becomes evident that when it comes to stock valuation, the influence of meat, poultry, fish, and eggs is not just a side dish—it's a main course. This study, while shedding light on a "beefy" relationship, proves that the intersection of dietary habits and stock market dynamics is no yolk. The implications for investors and market analysts are indeed "egg-citing," prompting a deeper understanding of the tantalizing connection between what's on our plates and what's on the stock exchange.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price (ANSS). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price"

[[ABSTRACT]]
This paper investigates the potential relationship between annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for the period from 2002 to 2022, a correlation coefficient of 0.9588266 and p < 0.01 was found, indicating a strong positive correlation between the two variables. The findings of this study suggest that changes in consumer spending on animal products may have a noticeable impact on the stock performance of ANSYs. It's clear that when it comes to market trends, the stakes are high, but this research suggests that poultry is no chicken when it comes to influencing stock prices.

[[INTRDUCTION]]
The consumption habits and preferences of the American household have long been a topic of interest for economists and market analysts alike. In recent years, the relationship between consumer spending and stock prices has garnered increasing attention, with researchers seeking to uncover potential correlations and causative factors. Our study contributes to this burgeoning field by examining the connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs, a leading player in the animal protein industry. We aim to shed light on whether the financial performance of ANSYs is correlated with the carnivorous inclinations of the American populace.
In the pursuit of this investigation, we have employed a robust dataset spanning the period from 2002 to 2022, sourced from the venerable Bureau of Labor Statistics and LSEG Analytics (Refinitiv). Our analysis revealed a striking correlation coefficient of 0.9588266 and p < 0.01, signifying a robust positive correlation between annual household expenditures on animal-derived sustenance and the stock price of ANSYs. The results of our study suggest that fluctuations in consumer spending on meat, poultry, fish, and eggs may exert a discernible influence on the performance of ANSYs in the stock market. It appears that when it comes to the financial realm, the cluck stops here for poultry products.
As we delve into the nuances of this correlation, it is essential to recognize the multifaceted factors that interplay within the domain of consumer spending and stock performance. While correlation does not imply causation, the empirical evidence presented in this study invites further scrutiny into the potential mechanisms through which dietary habits intersect with investment behaviors. The implications of these findings extend beyond mere market analysis, delving into the underlying societal patterns and economic forces that shape the contemporary landscape of commerce.
Our endeavor is underpinned by the conviction that seemingly disparate domains—culinary predilections and stock market dynamics—can intertwine in unforeseen ways, yielding insights that transcend conventional boundaries. Through unearthing these connections, we aim to carve a niche for interdisciplinary inquiry, where the seemingly esoteric realm of consumer behavior intertwines with the rigor of financial analysis. As we embark on this scholarly quest, we invite the reader to join us in uncovering the meaty fabric of economic interactions, recognizing that in the realm of financial analysis, there's always more than one way to slice the data.

[[RESULTS]]
The statistical analysis of the relationship between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs yielded intriguing results. Over the period from 2002 to 2022, a remarkably high correlation coefficient of 0.9588266 was observed, accompanied by an r-squared value of 0.9193484. Additionally, the p-value was found to be less than 0.01, indicating a statistically significant association between the variables under scrutiny.
As shown in Figure 1, the scatterplot vividly illustrates the conspicuous positive correlation between annual household expenditures on animal-derived sustenance and the stock price of ANSYs. It seems that when it comes to financial analysis, the relationship between consumers' carnivorous tendencies and stock market performance is not simply a matter of "chicken feed."
The substantial correlation coefficient and r-squared value indicate that approximately 91.9% of the variance in ANSYs' stock price can be explained by changes in annual US household spending on meat, poultry, fish, and eggs. These findings imply that the consumption patterns of carnivorous culinary delights play a significant role in shaping the market behavior of ANSYs. When it comes to the stock market, it appears that the meat of the matter lies in consumer preferences for animal-derived protein.
The profound implications of these results prompt further inquiry into the intricate mechanisms through which consumer spending habits intersect with stock market dynamics. Delving into the meaty fabric of economic interactions, this research underlines the interconnectedness of seemingly incongruous realms, demonstrating that financial analysis is not always a cut and dried affair.
In light of these findings, it is evident that the stock performance of ANSYs is intricately intertwined with the ebb and flow of consumer spending on meat, poultry, fish, and eggs. This correlation underscores the significance of animal-derived sustenance not only in culinary contexts but also in the intricate dance of market trends. It goes to show that when it comes to the stock market, the bullish behavior of consumers towards meat and animal products is nothing to snicker at.
It is essential to recognize that while correlation does not imply causation, the robust statistical relationship uncovered in this study opens the door to further investigation into the underlying mechanisms at play. As we chew over these findings, the tantalizing prospect of uncovering the steakholder interests at play in the intersection of culinary indulgences and financial phenomena comes to the fore. With a mix of statistical rigor and a pinch of humor, this study whets the appetite for future research endeavors that aim to unearth the sizzling dynamics of consumer behavior and stock market performance.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has revealed a robust and statistically significant correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of ANSYs. The findings of this study suggest that the market performance of ANSYs is inextricably linked to the carnivorous predilections of the American populace. It seems that in the realm of financial analysis, the value of consumer preferences for animal-derived sustenance is not to be butchered.

These findings open the door to further investigation into the mechanisms underlying this relationship. While we've sliced and diced the data, the steakholders of this correlation remain to be fully grilled. Future research endeavors may seek to explore the steakholder interests at play, shedding light on the sizzling dynamics of consumer behavior and stock market performance. However, it seems that when it comes to the connection between meaty cravings and stock prices, the meat of the matter has been well and truly tenderized.

As we turn over this well-done study, it becomes clear that no more research is needed to uncover the sizzling dynamics of consumer behavior and stock market performance in relation to annual US household spending on meats, poultry, fish, and eggs. The rare insights revealed herein have seasoned the academic pan with a flavor that cannot be overcooked. Time to take a break and grab a steak – the conclusion is a wrap!

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 Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Culinary Economics

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Culinary Consumption Studies

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price with an abstract of This paper investigates the potential relationship between annual US household spending on meats, poultry, fish, and eggs and ANSYs' stock price. Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for the period from 2002 to 2022, a correlation coefficient of 0.9588266 and p < 0.01 was found, indicating a strong positive correlation between the two variables. The findings of this study suggest that changes in consumer spending on animal products may have a noticeable impact on the stock performance of ANSYs. It's clear that when it comes to market trends, the stakes are high, but this research suggests that poultry is no chicken when it comes to influencing stock prices.

ChatGPT:

annual US household spending on meats, poultry, fish, eggs, ANSYs stock price, correlation study, consumer spending on meat, poultry, fish, eggs, stock market trends, Bureau of Labor Statistics data, LSEG Analytics, Refinitiv, stock performance, market trends, impact of consumer spending, poultry consumption, stock price correlation

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

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Data details

Annual US household spending on meats, poultry, fish, and eggs
Detailed data title: Average annual household spend on meats, poultry, fish, and eggs
Source: Bureau of Labor Statistics
See what else correlates with Annual US household spending on meats, poultry, fish, and eggs

ANSYs' stock price (ANSS)
Detailed data title: Opening price of ANSYS (ANSS) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: ^{See full details} Via Microsoft Excel Stockhistory function

See what else correlates with ANSYs' stock price (ANSS)

Correlation r = 0.9588266 (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 = 0.9193484 (Coefficient of determination)
This means 91.9% of the change in the one variable (i.e., ANSYs' stock price (ANSS)) is predictable based on the change in the other (i.e., Annual US household spending on meats, poultry, fish, and eggs) over the 21 years from 2002 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 7.7E-12.^Show 0.0000000000076937112226665300
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note 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 0.96 in 7.7E-10% of random cases. Said differently, if you correlated 129,976,284,664 random variables^Note You don't actually need 129 billion 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 20 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 20 because we have two variables measured over a period of 21 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.9, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} 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: ^Note 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	2022
Annual US household spending on meats, poultry, fish, and eggs (Household spend)	798	825	880	764	797	777	846	841	784	832	852	856	892	896	890	944	961	980	1075	1115	1216
ANSYs' stock price (ANSS) (Stock price)	6.12	5.03	9.89	16.22	21.27	21.63	41.32	27.86	43.56	52.55	58.06	68.54	86.44	82.09	91.4	93.12	148.15	140.19	257.01	362.35	398.95

Why this works

Data dredging: I have 25,213 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 635,695,369 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^Note 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^Note 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. ^Note 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. ^{Show the steps to do this.} 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([798,825,880,764,797,777,846,841,784,832,852,856,892,896,890,944,961,980,1075,1115,1216,])
array_2 = np.array([6.12,5.03,9.89,16.22,21.27,21.63,41.32,27.86,43.56,52.55,58.06,68.54,86.44,82.09,91.4,93.12,148.15,140.19,257.01,362.35,398.95,])
array_1_name = "Annual US household spending on meats, poultry, fish, and eggs"
array_2_name = "ANSYs' stock price (ANSS)"

# 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. ^Note 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.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note 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)
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Line chart for only Annual US household spending on meats, poultry, fish, and eggs
Line chart for only ANSYs' stock price (ANSS)
AI-generated correlation image
The spurious research paper: Meating Expectations: A Correlation Study of Annual US Household Spending on Meats, Poultry, Fish, and Eggs and ANSYs' Stock Price

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Correlation ID: 2283 · Black Variable ID: 19888 · Red Variable ID: 1849


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