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Spurious correlation #2,186 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Dallas and the second variable is Viewership count for Days of Our Lives.  The chart goes from 1980 to 2021, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As the air got cleaner in Dallas, it seems the Days of Our Lives just couldn't take the fresh plot twists anymore. The decrease in air pollution must have aired out the drama, leaving viewers with less smoggy storylines to follow. It's as if the show's ratings took a deep breath of fresh air and decided to take a permanent hiatus. Seems like the only thing not getting a clean break was the correlation between air quality and soap opera viewership in the city.

Model: dalle-3
Prompt: Generate an image in the style of a Polaroid photo that captures the hazy skyline of Dallas, with the smog settling heavily over the city. In the foreground, depict a group of dedicated fans gathered around their TV screens, silhouetted against the backdrop of the polluted sky, eagerly watching the latest episode of Days of Our Lives. Their animated expressions and intense focus on the show convey their unwavering loyalty and dedication as viewers. 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: Unhealthy air quality in Dallas and Viewership count for Days of Our Lives. 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: Macabre Sci-fi fantacy Symbolism Psychedelic a Polaroid photo
ChatGPT: a Polaroid photo

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 Unhealthy air quality in Dallas and Viewership count for Days of Our Lives together. Keep the description simple.
ChatGPT: The sky in Dallas is thick with smog and haze, making it difficult to see far into the distance. Despite the poor air quality, a group of dedicated fans gathers in front of their TV screens, eagerly tuning in to watch the latest episode of Days of Our Lives, their loyal viewership count adding to the show's popularity.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ a Polaroid photo ] and depicting this scene: The sky in Dallas is thick with smog and haze, making it difficult to see far into the distance. Despite the poor air quality, a group of dedicated fans gathers in front of their TV screens, eagerly tuning in to watch the latest episode of Days of Our Lives, their loyal viewership count adding to the show's popularity. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image in the style of a Polaroid photo that captures the hazy skyline of Dallas, with the smog settling heavily over the city. In the foreground, depict a group of dedicated fans gathered around their TV screens, silhouetted against the backdrop of the polluted sky, eagerly watching the latest episode of Days of Our Lives. Their animated expressions and intense focus on the show convey their unwavering loyalty and dedication as viewers.

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

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

Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how a decrease in Air pollution in Dallas caused Viewership count for Days of Our Lives to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)
The Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count

Journal of Environmental Pop Culture Studies

Jan 2024

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




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

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Air pollution in Dallas and Viewership count for Days of Our Lives. Make lots of jokes, goofy observations, and puns.

Make the title a clever pun.

Your research team used data from Environmental Protection Agency and Wikipedia to assess this nagging question. You found a correlation coefficient of 0.7721108 and p < 0.01 for 1980 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 Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count

[[ABSTRACT]]
In this study, we set out to explore the unexpected relationship between air pollution in Dallas and the viewership count for the beloved soap opera "Days of Our Lives." Leveraging data from the Environmental Protection Agency and Wikipedia, our research team embarked on a whimsical journey to unravel this peculiar correlation. To our surprise, we uncovered a statistically significant correlation coefficient of 0.7721108 with a p-value less than 0.01 for the period spanning 1980 to 2021.

Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. We invite readers to join us in this lighthearted exploration of the unanticipated links between air quality and daytime drama viewership.


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 Air pollution in Dallas and Viewership count for Days of Our Lives. Make lots of jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
The Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count

[[ABSTRACT]]
In this study, we set out to explore the unexpected relationship between air pollution in Dallas and the viewership count for the beloved soap opera "Days of Our Lives." Leveraging data from the Environmental Protection Agency and Wikipedia, our research team embarked on a whimsical journey to unravel this peculiar correlation. To our surprise, we uncovered a statistically significant correlation coefficient of 0.7721108 with a p-value less than 0.01 for the period spanning 1980 to 2021.

Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. We invite readers to join us in this lighthearted exploration of the unanticipated links between air quality and daytime drama viewership.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

Ah, the whimsical world of academic research! Today, dear readers, we embark on a journey to unravel the unexpected and sensational connection between air pollution in Dallas and the viewership count for the timeless soap opera "Days of Our Lives." While it may seem like an unlikely pair, our curiosity has been piqued, and we are excited to delve into the statistical labyrinth to uncover the mysteries that lie within.

As scholars, we often find ourselves submerged in the depths of serious and weighty research topics, but every now and then, a peculiar correlation emerges, beckoning us to set aside our conventional expectations and embrace the unexpected. Such is the story of our pursuit to examine the influence of Dallas air pollution on the viewership patterns of this beloved soap opera.

Now, you might be wondering why on earth anyone would consider the correlation between smog and soap operas. Well, let us assure you that we initially met this proposition with a healthy dose of skepticism and a raised eyebrow. Nevertheless, armed with data from the Environmental Protection Agency and Wikipedia, we ventured forth, prepared for the unexpected twists and turns that this expedition promised.

So, dear colleagues, let us buckle up, grab a bag of popcorn, and prepare ourselves for a rollercoaster ride through the land of statistical analysis, where soap opera drama intersects with environmental impact in the most peculiar of ways. Fasten your seatbelts and get ready for an adventure that will leave you pondering the perplexities of daytime television and air quality. It’s a ride you won't want to miss!


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 Air pollution in Dallas and Viewership count for Days of Our Lives. 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 Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count

[[ABSTRACT]]
In this study, we set out to explore the unexpected relationship between air pollution in Dallas and the viewership count for the beloved soap opera "Days of Our Lives." Leveraging data from the Environmental Protection Agency and Wikipedia, our research team embarked on a whimsical journey to unravel this peculiar correlation. To our surprise, we uncovered a statistically significant correlation coefficient of 0.7721108 with a p-value less than 0.01 for the period spanning 1980 to 2021.

Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. We invite readers to join us in this lighthearted exploration of the unanticipated links between air quality and daytime drama viewership.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Ah, the whimsical world of academic research! Today, dear readers, we embark on a journey to unravel the unexpected and sensational connection between air pollution in Dallas and the viewership count for the timeless soap opera "Days of Our Lives." While it may seem like an unlikely pair, our curiosity has been piqued, and we are excited to delve into the statistical labyrinth to uncover the mysteries that lie within.
As scholars, we often find ourselves submerged in the depths of serious and weighty research topics, but every now and then, a peculiar correlation emerges, beckoning us to set aside our conventional expectations and embrace the unexpected. Such is the story of our pursuit to examine the influence of Dallas air pollution on the viewership patterns of this beloved soap opera.
Now, you might be wondering why on earth anyone would consider the correlation between smog and soap operas. Well, let us assure you that we initially met this proposition with a healthy dose of skepticism and a raised eyebrow. Nevertheless, armed with data from the Environmental Protection Agency and Wikipedia, we ventured forth, prepared for the unexpected twists and turns that this expedition promised.
So, dear colleagues, let us buckle up, grab a bag of popcorn, and prepare ourselves for a rollercoaster ride through the land of statistical analysis, where soap opera drama intersects with environmental impact in the most peculiar of ways. Fasten your seatbelts and get ready for an adventure that will leave you pondering the perplexities of daytime television and air quality. It’s a ride you won't want to miss!

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

In "Air Pollution and Its Effects on Human Health and the Environment" by Smith, the authors find a comprehensive analysis of the impact of air pollution on respiratory diseases, cardiovascular health, and environmental degradation. While the focus is on human health and ecological repercussions, the study provides a foundational understanding of the pervasive influence of air pollution in urban areas such as Dallas.

Doe's "The Economics of Air Pollution" offers insights into the economic implications of air quality, shedding light on the societal costs associated with pollution mitigation and public health expenditures. This economic perspective forms a critical backdrop for our examination of the intersection between air pollution in Dallas and its unforeseen relationship with television viewership.

Jones's "Urban Air Quality Management Strategy in Developing Countries" presents strategies for managing urban air quality, including case studies on cities facing similar environmental challenges as Dallas. These scholarly works anchor our investigation amid the backdrop of urban air quality management and serve as a springboard for our whimsical exploration.

Turning to popular non-fiction works, "The Air We Breathe: A Regional Examination of Air Quality in the United States" delves into the specifics of air pollution in various regions, including extensive analysis of the Dallas metropolitan area. The granular insights provided in this source help contextualize our investigation within the unique environmental landscape of Dallas.

On the fictional front, "Smoke and Mirrors: A Soap Opera Saga" brings the melodramatic world of soap operas to the forefront, intertwining tales of love, betrayal, and unforeseen plot twists. Though not a scientific treatise, the intrigue and captivation within soap operas provide an intriguing parallel to our exploration of the unexpected correlation between Dallas air pollution and soap opera viewership.

In a departure from traditional research avenues, our literature review also encompassed a thorough examination of unexpected sources. This unorthodox approach led us to delve into the back covers of various household items, including shampoo bottles, where we stumbled upon unexpected insights into the quirks of everyday life. While unconventional, this unconventional strategy provided us with a unique perspective on the interplay between environmental factors and leisure preferences.

As we synthesize the varied literature, we invite readers to embrace the lighthearted spirit of our investigation, where scholarly rigor meets the whimsical allure of daytime drama and environmental intrigue. Join us on this unexpected journey, where statistical analysis intertwines with the serendipitous world of televised entertainment and atmospheric whimsy.


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 Air pollution in Dallas and Viewership count for Days of Our Lives. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Wikipedia . You used data from 1980 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 Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count

[[ABSTRACT]]
In this study, we set out to explore the unexpected relationship between air pollution in Dallas and the viewership count for the beloved soap opera "Days of Our Lives." Leveraging data from the Environmental Protection Agency and Wikipedia, our research team embarked on a whimsical journey to unravel this peculiar correlation. To our surprise, we uncovered a statistically significant correlation coefficient of 0.7721108 with a p-value less than 0.01 for the period spanning 1980 to 2021.

Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. We invite readers to join us in this lighthearted exploration of the unanticipated links between air quality and daytime drama viewership.

[[INTRODUCTION]]
Ah, the whimsical world of academic research! Today, dear readers, we embark on a journey to unravel the unexpected and sensational connection between air pollution in Dallas and the viewership count for the timeless soap opera "Days of Our Lives." While it may seem like an unlikely pair, our curiosity has been piqued, and we are excited to delve into the statistical labyrinth to uncover the mysteries that lie within.
As scholars, we often find ourselves submerged in the depths of serious and weighty research topics, but every now and then, a peculiar correlation emerges, beckoning us to set aside our conventional expectations and embrace the unexpected. Such is the story of our pursuit to examine the influence of Dallas air pollution on the viewership patterns of this beloved soap opera.
Now, you might be wondering why on earth anyone would consider the correlation between smog and soap operas. Well, let us assure you that we initially met this proposition with a healthy dose of skepticism and a raised eyebrow. Nevertheless, armed with data from the Environmental Protection Agency and Wikipedia, we ventured forth, prepared for the unexpected twists and turns that this expedition promised.
So, dear colleagues, let us buckle up, grab a bag of popcorn, and prepare ourselves for a rollercoaster ride through the land of statistical analysis, where soap opera drama intersects with environmental impact in the most peculiar of ways. Fasten your seatbelts and get ready for an adventure that will leave you pondering the perplexities of daytime television and air quality. It’s a ride you won't want to miss!

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To unravel the enigmatic connection between air pollution in Dallas and the viewership count for "Days of Our Lives," our research team embarked on a whimsical data expedition that would make even the most intrepid explorers raise an eyebrow in curiosity. Armed with an arsenal of statistical tools and a twinkle of wonder in our eyes, we set forth to navigate the uncharted territory where environmental quality mingles with daytime drama aficionados.

Firstly, we scoured the digital depths of the internet, traversing the virtual landscapes of the Environmental Protection Agency's archives and traversing the avenues of Wikipedia. Our data collection odyssey led us to compile air quality metrics in Dallas, encompassing a timeframe from 1980 to 2021. These metrics included but were not limited to air pollutant concentrations, such as particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3). We chose this broad time span to capture the ever-changing climatic, societal, and technological influences that could potentially sway the relationship between air pollution and soap opera viewership.

In parallel to our environmental odyssey, we delved into the world of soap opera fandom, trailing the viewership count for "Days of Our Lives" across the same chronological span. This endeavor involved sourcing viewership data from a variety of reliable sources, including Nielsen ratings, television network archives, and online databases dedicated to broadcasting statistics.

With our treasure trove of data in hand, we then plunged headfirst into the statistical labyrinth, donning our virtual explorer hats and cracking our metaphorical statistical whip. We meticulously employed sophisticated analytical techniques, including correlation analysis, regression modeling, and time series analysis, to tease out the whimsical relationship between Dallas air pollution and the viewership count for "Days of Our Lives."

Our primary objective was to procure a nuanced understanding of how variations in air quality metrics may influence the ebb and flow of soap opera viewership. Specifically, our analysis aimed to discern whether the fluctuations in air pollution levels whimsically coincide with the fluctuations in the audience's enchantment with the timeless drama that unfolds in the world of Salem.

While the path we traversed may have been unconventional, and the quest itself might appear to defy traditional research norms, we fervently believe that our findings will not only entertain but also enthrall the academic community. So buckle up, dear readers, as we elucidate the meticulous detail and the whimsical charm of our methodological symphony that drew the unexpected correlation between smog and soap operas into the limelight of statistical significance.


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 Air pollution in Dallas and Viewership count for Days of Our Lives. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Wikipedia .

For the time period 1980 to 2021, you found a correlation 0.7721108, r-squared of 0.5961551, 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 Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count

[[ABSTRACT]]
In this study, we set out to explore the unexpected relationship between air pollution in Dallas and the viewership count for the beloved soap opera "Days of Our Lives." Leveraging data from the Environmental Protection Agency and Wikipedia, our research team embarked on a whimsical journey to unravel this peculiar correlation. To our surprise, we uncovered a statistically significant correlation coefficient of 0.7721108 with a p-value less than 0.01 for the period spanning 1980 to 2021.

Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. We invite readers to join us in this lighthearted exploration of the unanticipated links between air quality and daytime drama viewership.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Ah, the whimsical world of academic research! Today, dear readers, we embark on a journey to unravel the unexpected and sensational connection between air pollution in Dallas and the viewership count for the timeless soap opera "Days of Our Lives." While it may seem like an unlikely pair, our curiosity has been piqued, and we are excited to delve into the statistical labyrinth to uncover the mysteries that lie within.
As scholars, we often find ourselves submerged in the depths of serious and weighty research topics, but every now and then, a peculiar correlation emerges, beckoning us to set aside our conventional expectations and embrace the unexpected. Such is the story of our pursuit to examine the influence of Dallas air pollution on the viewership patterns of this beloved soap opera.
Now, you might be wondering why on earth anyone would consider the correlation between smog and soap operas. Well, let us assure you that we initially met this proposition with a healthy dose of skepticism and a raised eyebrow. Nevertheless, armed with data from the Environmental Protection Agency and Wikipedia, we ventured forth, prepared for the unexpected twists and turns that this expedition promised.
So, dear colleagues, let us buckle up, grab a bag of popcorn, and prepare ourselves for a rollercoaster ride through the land of statistical analysis, where soap opera drama intersects with environmental impact in the most peculiar of ways. Fasten your seatbelts and get ready for an adventure that will leave you pondering the perplexities of daytime television and air quality. It’s a ride you won't want to miss!

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

Upon delving into the depths of our data analysis, we uncovered a surprising and quite "airy" correlation between air pollution in Dallas and the viewership count for "Days of Our Lives." The correlation coefficient of 0.7721108, coupled with an r-squared value of 0.5961551 and a p-value less than 0.01, left our research team quite astonished, to say the least. It seems that the atmospheric drama of Dallas may just have an unexpected impact on the on-screen drama of our beloved soap opera.

Our scatterplot (Fig. 1) tells a whimsical tale of its own, illustrating the strong correlation between these seemingly disparate variables. Who would have thought that the haze hanging over Dallas could cast such an intriguing shadow on soap opera viewership? As we gazed upon this scatterplot, we couldn't help but imagine the characters of "Days of Our Lives" navigating their own existential haze amidst the ebb and flow of Dallas air pollution.

While our findings may seem fantastical to some, they present a tangible link between environmental factors and the preferences of daytime television viewers. This unexpected correlation prompts us to ponder the whimsical ways in which external influences may shape our entertainment choices.

In light of these intriguing results, we urge readers to join us in this light-hearted exploration of the surprising connections between air quality and soap opera enthusiasts. After all, who knew that the skies of Dallas could hold so much sway over the saga of Salem? Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences.

In conclusion, our statistical findings shed a lighthearted and unexpected light on the intersection of air pollution and the drama that unfolds on daytime television screens. As we pack up our statistical tools and bid farewell to this peculiar adventure, we leave with a newfound appreciation for the whimsy that can be uncovered when we delve into uncharted territories of research. Join us as we set sail for the next thrilling statistical odyssey, where unpredictability reigns supreme, and the unexpected is always just around the corner.


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 Air pollution in Dallas and Viewership count for Days of Our Lives. 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 Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count

[[ABSTRACT]]
In this study, we set out to explore the unexpected relationship between air pollution in Dallas and the viewership count for the beloved soap opera "Days of Our Lives." Leveraging data from the Environmental Protection Agency and Wikipedia, our research team embarked on a whimsical journey to unravel this peculiar correlation. To our surprise, we uncovered a statistically significant correlation coefficient of 0.7721108 with a p-value less than 0.01 for the period spanning 1980 to 2021.

Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. We invite readers to join us in this lighthearted exploration of the unanticipated links between air quality and daytime drama viewership.

[[LITERATURE REVIEW]]
In "Air Pollution and Its Effects on Human Health and the Environment" by Smith, the authors find a comprehensive analysis of the impact of air pollution on respiratory diseases, cardiovascular health, and environmental degradation. While the focus is on human health and ecological repercussions, the study provides a foundational understanding of the pervasive influence of air pollution in urban areas such as Dallas.
Doe's "The Economics of Air Pollution" offers insights into the economic implications of air quality, shedding light on the societal costs associated with pollution mitigation and public health expenditures. This economic perspective forms a critical backdrop for our examination of the intersection between air pollution in Dallas and its unforeseen relationship with television viewership.
Jones's "Urban Air Quality Management Strategy in Developing Countries" presents strategies for managing urban air quality, including case studies on cities facing similar environmental challenges as Dallas. These scholarly works anchor our investigation amid the backdrop of urban air quality management and serve as a springboard for our whimsical exploration.
Turning to popular non-fiction works, "The Air We Breathe: A Regional Examination of Air Quality in the United States" delves into the specifics of air pollution in various regions, including extensive analysis of the Dallas metropolitan area. The granular insights provided in this source help contextualize our investigation within the unique environmental landscape of Dallas.
On the fictional front, "Smoke and Mirrors: A Soap Opera Saga" brings the melodramatic world of soap operas to the forefront, intertwining tales of love, betrayal, and unforeseen plot twists. Though not a scientific treatise, the intrigue and captivation within soap operas provide an intriguing parallel to our exploration of the unexpected correlation between Dallas air pollution and soap opera viewership.
In a departure from traditional research avenues, our literature review also encompassed a thorough examination of unexpected sources. This unorthodox approach led us to delve into the back covers of various household items, including shampoo bottles, where we stumbled upon unexpected insights into the quirks of everyday life. While unconventional, this unconventional strategy provided us with a unique perspective on the interplay between environmental factors and leisure preferences.
As we synthesize the varied literature, we invite readers to embrace the lighthearted spirit of our investigation, where scholarly rigor meets the whimsical allure of daytime drama and environmental intrigue. Join us on this unexpected journey, where statistical analysis intertwines with the serendipitous world of televised entertainment and atmospheric whimsy.

[[RESULTS]]
Upon delving into the depths of our data analysis, we uncovered a surprising and quite "airy" correlation between air pollution in Dallas and the viewership count for "Days of Our Lives." The correlation coefficient of 0.7721108, coupled with an r-squared value of 0.5961551 and a p-value less than 0.01, left our research team quite astonished, to say the least. It seems that the atmospheric drama of Dallas may just have an unexpected impact on the on-screen drama of our beloved soap opera.
Our scatterplot (Fig. 1) tells a whimsical tale of its own, illustrating the strong correlation between these seemingly disparate variables. Who would have thought that the haze hanging over Dallas could cast such an intriguing shadow on soap opera viewership? As we gazed upon this scatterplot, we couldn't help but imagine the characters of "Days of Our Lives" navigating their own existential haze amidst the ebb and flow of Dallas air pollution.
While our findings may seem fantastical to some, they present a tangible link between environmental factors and the preferences of daytime television viewers. This unexpected correlation prompts us to ponder the whimsical ways in which external influences may shape our entertainment choices.
In light of these intriguing results, we urge readers to join us in this light-hearted exploration of the surprising connections between air quality and soap opera enthusiasts. After all, who knew that the skies of Dallas could hold so much sway over the saga of Salem? Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences.
In conclusion, our statistical findings shed a lighthearted and unexpected light on the intersection of air pollution and the drama that unfolds on daytime television screens. As we pack up our statistical tools and bid farewell to this peculiar adventure, we leave with a newfound appreciation for the whimsy that can be uncovered when we delve into uncharted territories of research. Join us as we set sail for the next thrilling statistical odyssey, where unpredictability reigns supreme, and the unexpected is always just around the corner.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our findings have brought to light a fascinating correlation between air pollution in the Dallas metropolitan area and the viewership count for "Days of Our Lives." As we contemplate the interplay of atmospheric whimsy and daytime drama, it becomes evident that the unexpected relationship between these seemingly disparate entities holds significant implications for our understanding of leisure preferences and environmental influences. Let's delve into the implications of our results and how they align with the existing body of research, both lighthearted and serious.

The scholarly literature has traditionally focused on the tangible health and economic implications of air pollution. However, our research introduces a playfully unique twist by uncovering an unexpected parallel between Dallas's atmospheric drama and the on-screen saga of "Days of Our Lives." The whimsical intersection of scholarly rigor and televised entertainment has led us to ponder the lighthearted ways in which external factors may shape our leisure preferences. Our study aligns with the foundational understanding established by Smith's analysis of the impact of air pollution on human health, as it illuminates the unanticipated influence of air quality on leisure choices. Furthermore, Doe's exploration of the economic implications of air pollution provides a fitting backdrop for our findings, as it underscores the multifaceted reach of environmental factors into seemingly unrelated domains, such as television viewership.

It is worth noting that our research has taken a departure from traditional avenues by embracing the unexpected sources such as non-scientific treatises like "Smoke and Mirrors: A Soap Opera Saga." This unconventional approach has provided an intriguing parallel to our exploration of the unexpected correlation between Dallas air pollution and soap opera viewership. In doing so, we have highlighted the importance of approaching research with a lighthearted spirit, where statistical analysis intertwines with the serendipitous world of televised entertainment and atmospheric whimsy. While our findings may seem fantastical to some, they offer a refreshing perspective on the potential interplay between external environmental influences and leisure choices.

As we consider the implications of our results, it becomes apparent that our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. The unexpected nature of our correlation underscores the need for continued exploration of the unanticipated links between environmental influences and leisure activities. Our research beckons for a lighthearted approach to scholarly inquiries, where unpredictability reigns supreme, and the unexpected is always just around the corner.

In conclusion, our whimsical investigation into the correlation between Dallas air pollution and soap opera viewership provides a unique contribution to the interdisciplinary landscape of environmental influences and leisure preferences. As we step back from this peculiar adventure, we do so with a newfound appreciation for the unexpected whimsy that can be uncovered when we delve into uncharted territories of research. Our findings serve as a reminder that the lighthearted exploration of statistical odysseys can yield invaluable insights into the serendipitous interplay between seemingly disparate elements. Join us as we set sail for the next thrilling statistical odyssey, for in the whimsical world of scholarly research, the unexplored holds promise for unexpected connections and lighthearted revelations.


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 Air pollution in Dallas and Viewership count for Days of Our Lives. 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 Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count

[[ABSTRACT]]
In this study, we set out to explore the unexpected relationship between air pollution in Dallas and the viewership count for the beloved soap opera "Days of Our Lives." Leveraging data from the Environmental Protection Agency and Wikipedia, our research team embarked on a whimsical journey to unravel this peculiar correlation. To our surprise, we uncovered a statistically significant correlation coefficient of 0.7721108 with a p-value less than 0.01 for the period spanning 1980 to 2021.

Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. We invite readers to join us in this lighthearted exploration of the unanticipated links between air quality and daytime drama viewership.

[[INTRDUCTION]]
Ah, the whimsical world of academic research! Today, dear readers, we embark on a journey to unravel the unexpected and sensational connection between air pollution in Dallas and the viewership count for the timeless soap opera "Days of Our Lives." While it may seem like an unlikely pair, our curiosity has been piqued, and we are excited to delve into the statistical labyrinth to uncover the mysteries that lie within.
As scholars, we often find ourselves submerged in the depths of serious and weighty research topics, but every now and then, a peculiar correlation emerges, beckoning us to set aside our conventional expectations and embrace the unexpected. Such is the story of our pursuit to examine the influence of Dallas air pollution on the viewership patterns of this beloved soap opera.
Now, you might be wondering why on earth anyone would consider the correlation between smog and soap operas. Well, let us assure you that we initially met this proposition with a healthy dose of skepticism and a raised eyebrow. Nevertheless, armed with data from the Environmental Protection Agency and Wikipedia, we ventured forth, prepared for the unexpected twists and turns that this expedition promised.
So, dear colleagues, let us buckle up, grab a bag of popcorn, and prepare ourselves for a rollercoaster ride through the land of statistical analysis, where soap opera drama intersects with environmental impact in the most peculiar of ways. Fasten your seatbelts and get ready for an adventure that will leave you pondering the perplexities of daytime television and air quality. It’s a ride you won't want to miss!

[[RESULTS]]
Upon delving into the depths of our data analysis, we uncovered a surprising and quite "airy" correlation between air pollution in Dallas and the viewership count for "Days of Our Lives." The correlation coefficient of 0.7721108, coupled with an r-squared value of 0.5961551 and a p-value less than 0.01, left our research team quite astonished, to say the least. It seems that the atmospheric drama of Dallas may just have an unexpected impact on the on-screen drama of our beloved soap opera.
Our scatterplot (Fig. 1) tells a whimsical tale of its own, illustrating the strong correlation between these seemingly disparate variables. Who would have thought that the haze hanging over Dallas could cast such an intriguing shadow on soap opera viewership? As we gazed upon this scatterplot, we couldn't help but imagine the characters of "Days of Our Lives" navigating their own existential haze amidst the ebb and flow of Dallas air pollution.
While our findings may seem fantastical to some, they present a tangible link between environmental factors and the preferences of daytime television viewers. This unexpected correlation prompts us to ponder the whimsical ways in which external influences may shape our entertainment choices.
In light of these intriguing results, we urge readers to join us in this light-hearted exploration of the surprising connections between air quality and soap opera enthusiasts. After all, who knew that the skies of Dallas could hold so much sway over the saga of Salem? Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences.
In conclusion, our statistical findings shed a lighthearted and unexpected light on the intersection of air pollution and the drama that unfolds on daytime television screens. As we pack up our statistical tools and bid farewell to this peculiar adventure, we leave with a newfound appreciation for the whimsy that can be uncovered when we delve into uncharted territories of research. Join us as we set sail for the next thrilling statistical odyssey, where unpredictability reigns supreme, and the unexpected is always just around the corner.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In closing, our research has unveiled a delightfully "soapy" correlation between air pollution in Dallas and the viewership count for "Days of Our Lives." It appears that the atmospheric ambiance of Dallas may not only be affecting respiratory systems but also stirring the emotional inclinations of soap opera enthusiasts. As we bid adieu to this whimsical exploration, we can't help but marvel at the unexpected ludicrosity of our findings.

The statistical relationship we've uncovered serves as a gentle reminder that even the most peculiar pairings can have a connection that tickles the fancy of both academics and soap opera aficionados alike. We are left pondering the enigmatic ways in which external factors can mold our entertaining inclinations, and who would have thought that smog could have such an impact on soap opera drama?

As we part ways with this fanciful inquiry, we are left with a profound sense of amusement and intrigue. After all, the air pollution and soap opera saga may seem like a laughable pairing, but our statistical endeavor has presented us with a whimsical puzzle that tickles the mind. Given the breadth and depth of our findings, we can confidently say that no more research is needed in this area.

We now set our sights on the next thrilling statistical odyssey, where the unexpected is always just around the corner and where the laughter of ludicrous correlations echoes through the hallowed halls of academia. Join us as we embrace the capricious nature of research and continue to uncover the whimsical wonders that await in the world of academic exploration!


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 Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Environmental Pop Culture Studies


Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Environmental Aesthetics and Viewer Engagement


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 Days of Our Lives: An Airy Connection Between Dallas Air Pollution and Viewership Count with an abstract of In this study, we set out to explore the unexpected relationship between air pollution in Dallas and the viewership count for the beloved soap opera "Days of Our Lives." Leveraging data from the Environmental Protection Agency and Wikipedia, our research team embarked on a whimsical journey to unravel this peculiar correlation. To our surprise, we uncovered a statistically significant correlation coefficient of 0.7721108 with a p-value less than 0.01 for the period spanning 1980 to 2021.
Our findings not only entertain the possibility of an airy connection between air pollution and soap opera viewership but also prompt us to ponder the whimsical ways in which external factors can influence television preferences. We invite readers to join us in this lighthearted exploration of the unanticipated links between air quality and daytime drama viewership.

ChatGPT:

Dallas air pollution, soap opera viewership, correlation, Environmental Protection Agency data, Wikipedia data, air pollution and television preferences, air quality and soap opera viewership

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



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Data details

Air pollution in Dallas
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Dallas-Fort Worth-Arlington, TX
Source: Environmental Protection Agency
See what else correlates with Air pollution in Dallas

Viewership count for Days of Our Lives
Detailed data title: Metered viewership count for Days of Our Lives
Source: Wikipedia
See what else correlates with Viewership count for Days of Our Lives

Correlation r = 0.7721108 (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 = 0.5961551 (Coefficient of determination)
This means 59.6% of the change in the one variable (i.e., Viewership count for Days of Our Lives) is predictable based on the change in the other (i.e., Air pollution in Dallas) over the 42 years from 1980 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.1E-9. 0.0000000021284038299761897000
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 0.77 in 2.1E-7% of random cases. Said differently, if you correlated 469,835,651 random variables You don't actually need 469 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 41 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 41 because we have two variables measured over a period of 42 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.

[ 0.61, 0.87 ] 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.
198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018201920202021
Air pollution in Dallas (Bad air quality days)26.502723.561618.904125.479527.595625.479520.27422.739727.595618.630118.356216.438412.841512.60272022.739716.939919.72624.109621.917820.491821.095917.808218.630116.666723.287718.356212.328813.114813.97267.6712318.082216.939912.60277.3972612.05484.918036.575349.863017.945216.557388.49315
Viewership count for Days of Our Lives (Nielson share)560000055000005700000710000071000007200000700000071000006500000540000052000005400000490000056000005300000580000058000005100000580000042000003800000360000031000003100000270000026000002300000210000022000002200000200000020000002100000190000020500001800000160000016000001600000140000012000001200000




Why this works

  1. Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
  2. Lack of causal connection: There is probably Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
    no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
  3. Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
    p-value calculation does not take this into account, so mathematically it appears less probable than it really is.




Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."


# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([26.5027,23.5616,18.9041,25.4795,27.5956,25.4795,20.274,22.7397,27.5956,18.6301,18.3562,16.4384,12.8415,12.6027,20,22.7397,16.9399,19.726,24.1096,21.9178,20.4918,21.0959,17.8082,18.6301,16.6667,23.2877,18.3562,12.3288,13.1148,13.9726,7.67123,18.0822,16.9399,12.6027,7.39726,12.0548,4.91803,6.57534,9.86301,7.94521,6.55738,8.49315,])
array_2 = np.array([5600000,5500000,5700000,7100000,7100000,7200000,7000000,7100000,6500000,5400000,5200000,5400000,4900000,5600000,5300000,5800000,5800000,5100000,5800000,4200000,3800000,3600000,3100000,3100000,2700000,2600000,2300000,2100000,2200000,2200000,2000000,2000000,2100000,1900000,2050000,1800000,1600000,1600000,1600000,1400000,1200000,1200000,])
array_1_name = "Air pollution in Dallas"
array_2_name = "Viewership count for Days of Our Lives"

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

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



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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."

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Correlation ID: 2186 · Black Variable ID: 20511 · Red Variable ID: 87
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