Spurious correlation #1,678 · View random

AI explanation

As Super Bowl point differences widened, football fans became more accustomed to lopsided outcomes. This, in turn, made the subtle humor and nuanced plot developments of "Two and a Half Men" seem comparatively more exciting and unpredictable, leading to higher season ratings. It's like they say - a blowout game on the field makes for a blockbuster night of TV!

^{Show image generation prompt} Model: dalle-3
Prompt: Create an image of a TIME magazine cover that visually conveys the excitement of the Super Bowl. The cover features a football stadium with a close game score of 28-24 displayed on a prominent scoreboard. Add a TV screen next to the scoreboard, showing an episode of "Two and a Half Men" with a rating of 7.5 stars. Two characters from the show share a lighthearted moment in the living room. The image should capture the energy and anticipation of the game and the cozy atmosphere of the TV show scene. 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: Super Bowl point difference and Season rating of "Two and a Half Men". 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: 3D Pixar Style Macabre Macabre An image that looks like it is a scene from A Disney Movie A TIME magazine cover image
ChatGPT: A TIME magazine cover image

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 Super Bowl point difference and Season rating of "Two and a Half Men" together. Keep the description simple.
ChatGPT: In the scene, the Super Bowl point difference is represented by a scoreboard showing a close game with the final score of 28-24. Nearby, a TV screen displays an episode of "Two and a Half Men," with a rating of 7.5 stars, as two characters share a lighthearted moment in the living room.

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 TIME magazine cover image ] and depicting this scene: In the scene, the Super Bowl point difference is represented by a scoreboard showing a close game with the final score of 28-24. Nearby, a TV screen displays an episode of "Two and a Half Men," with a rating of 7.5 stars, as two characters share a lighthearted moment in the living room. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of a TIME magazine cover that visually conveys the excitement of the Super Bowl. The cover features a football stadium with a close game score of 28-24 displayed on a prominent scoreboard. Add a TV screen next to the scoreboard, showing an episode of "Two and a Half Men" with a rating of 7.5 stars. Two characters from the show share a lighthearted moment in the living room. The image should capture the energy and anticipation of the game and the cozy atmosphere of the TV show scene.

*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 Super Bowl point difference caused Season rating of "Two and a Half Men" to increase.

AI academic paper

(Because p < 0.01)

Pointing to Success: The Super Bowl Point Difference and the Rating of Two and a Half Men

Journal of Sports and Pop Culture Analysis

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 Super Bowl point difference and Season rating of "Two and a Half Men". 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 CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
Pointing to Success: The Super Bowl Point Difference and the Rating of "Two and a Half Men"

[[ABSTRACT]]
In this paper, we delve into the perplexing relationship between the point difference in Super Bowl games and the rating of the hit sitcom "Two and a Half Men." With punts, we mean points, and touchdowns, we aim to make a touchdown in understanding this quirky connection. Utilizing data from Wikipedia and Nielsen ratings, our research team has uncovered a statistically significant correlation coefficient of 0.7577800 and p < 0.01, spanning the years 2004 to 2015. Our findings suggest that there may be a peculiar link between the thrilling highs and disappointing lows of Super Bowl victories and the ratings of a sitcom filled with its own brand of comedic ups and downs. This study not only sheds light on an unexpected correlation but also shows the potential for quirky patterns in seemingly unrelated data. So, in the spirit of the Super Bowl, we invite readers to join us in this game of deciphering the "Two and a Half Men" rating mystery!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Introduction
In the realm of statistical analysis, one often encounters intriguing correlations that defy logical explanation at first glance. The connection between the point difference in Super Bowl games and the rating of the popular sitcom "Two and a Half Men" is one such enigmatic pairing that has piqued the curiosity of researchers and armchair statisticians alike. While the Super Bowl and a comedy series may seem as unrelated as a quarterback and a ballet dancer, our investigation has revealed a surprising relationship that calls for scrutiny.
The Super Bowl, with its heart-stopping plays and nail-biting finishes, captures the attention of millions, much like a captivating sitcom. The ebb and flow of excitement during the game mirrors the comedic highs and lows that "Two and a Half Men" delivers to its audience. It is within this realm of unpredictability and drama that we sought to unravel the puzzle of how the performance of football teams in the Super Bowl may manifest in the ratings of a television show centered around the comedic antics of Charlie Harper, Alan Harper, and Jake Harper.
Our research team combed through a trove of data from Wikipedia and Nielsen ratings, employing rigorous statistical analysis to uncover any semblance of a connection between the two seemingly disparate entities. Our pursuit led us down a path filled with unexpected twists and turns, akin to a thrilling Super Bowl matchup or a convoluted sitcom plot.
The correlation coefficient of 0.7577800 and p < 0.01 that emerged from our analysis signified a robust and statistically significant relationship between the Super Bowl point difference and the rating of "Two and a Half Men." This revelation left us both baffled and exhilarated, akin to the mix of emotions experienced by fans watching a game-deciding field goal attempt in the dying seconds of a Super Bowl showdown.
As we navigate through the labyrinthine world of data analysis, we are reminded of the words of acclaimed statistician, George Box, who famously said, "All models are wrong, but some are useful." Our findings, while unexpected, carry the potential to illuminate unconventional patterns lurking within the annals of data, perhaps serving as a reminder that correlations, like Super Bowl commercials, may hide in plain sight while bearing unexpected relevance.
Therefore, with the spirit of curiosity and a touch of lightheartedness, we invite our esteemed readers to join us in unraveling this delightful conundrum and to partake in the whimsical journey of deciphering the "Two and a Half Men" rating mystery in light of the grand spectacle that is the Super Bowl.
So, buckle up and prepare to tackle this puzzling connection with the same fervor as a team fighting for every yard on the gridiron, as we embark on a quest to fathom the quirkiness that binds the Super Bowl point difference and the rating of "Two and a Half Men" in delightful statistical harmony.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
LITERATURE REVIEW

In "Smith and Doe (2010)," the authors find an unexpected correlation between Super Bowl point differences and television ratings of popular sitcoms. The study provides a comprehensive analysis of various sports events and their impact on entertainment viewership. Similarly, "Jones et al. (2012)" delve into the viewership patterns of comedic television series in relation to major sporting events, including the Super Bowl, revealing intriguing connections that challenge conventional wisdom.

Moving beyond the realm of academic studies, non-fiction books such as "Freakonomics" by Steven D. Levitt and Stephen J. Dubner, and "Outliers" by Malcolm Gladwell, offer insights into the intricate web of correlations and the often perplexing relationships that underpin seemingly unrelated phenomena. These scholarly works nimbly navigate through the labyrinth of statistics and human behavior, shedding light on unexpected linkages that titillate the mind and tickle the funny bone.

Further expanding the literary landscape, fictional works such as "Moneyball" by Michael Lewis and "The Art of Fielding" by Chad Harbach, present captivating narratives that center around the dynamics of sports and the idiosyncrasies of human nature, offering a glimpse into the enthralling tapestry of interconnectedness and chance encounters.

And now, for the pièce de résistance of our literature review – our research team's groundbreaking investigation encompassing an eclectic array of sources, from scholarly journals to *checks notes* grocery store receipts and even the occasional fortune cookie message. Yes, dear reader, the pursuit of knowledge knows no bounds, and neither does the quest for the perfect punchline!

With our exploratory journey spanning the intellectual, the fantastical, and, dare I say, the comically absurd, we now shift our focus to unraveling the enigma that is the relationship between the Super Bowl point difference and the rating of "Two and a Half Men." Gird your loins, dear academics and aficionados of statistical whimsy, for we are about to embark on a rollercoaster of statistical analysis that will make even the most riveting Super Bowl showdown seem, dare I say, a tad predictable.

Let the games begin!

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 Super Bowl point difference and Season rating of "Two and a Half Men". Make lots of 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 Wikipedia and Wikipedia . You used data from 2004 to 2015

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]]
Pointing to Success: The Super Bowl Point Difference and the Rating of "Two and a Half Men"

[[ABSTRACT]]
In this paper, we delve into the perplexing relationship between the point difference in Super Bowl games and the rating of the hit sitcom "Two and a Half Men." With punts, we mean points, and touchdowns, we aim to make a touchdown in understanding this quirky connection. Utilizing data from Wikipedia and Nielsen ratings, our research team has uncovered a statistically significant correlation coefficient of 0.7577800 and p < 0.01, spanning the years 2004 to 2015. Our findings suggest that there may be a peculiar link between the thrilling highs and disappointing lows of Super Bowl victories and the ratings of a sitcom filled with its own brand of comedic ups and downs. This study not only sheds light on an unexpected correlation but also shows the potential for quirky patterns in seemingly unrelated data. So, in the spirit of the Super Bowl, we invite readers to join us in this game of deciphering the "Two and a Half Men" rating mystery!

[[INTRODUCTION]]
Introduction
In the realm of statistical analysis, one often encounters intriguing correlations that defy logical explanation at first glance. The connection between the point difference in Super Bowl games and the rating of the popular sitcom "Two and a Half Men" is one such enigmatic pairing that has piqued the curiosity of researchers and armchair statisticians alike. While the Super Bowl and a comedy series may seem as unrelated as a quarterback and a ballet dancer, our investigation has revealed a surprising relationship that calls for scrutiny.
The Super Bowl, with its heart-stopping plays and nail-biting finishes, captures the attention of millions, much like a captivating sitcom. The ebb and flow of excitement during the game mirrors the comedic highs and lows that "Two and a Half Men" delivers to its audience. It is within this realm of unpredictability and drama that we sought to unravel the puzzle of how the performance of football teams in the Super Bowl may manifest in the ratings of a television show centered around the comedic antics of Charlie Harper, Alan Harper, and Jake Harper.
Our research team combed through a trove of data from Wikipedia and Nielsen ratings, employing rigorous statistical analysis to uncover any semblance of a connection between the two seemingly disparate entities. Our pursuit led us down a path filled with unexpected twists and turns, akin to a thrilling Super Bowl matchup or a convoluted sitcom plot.
The correlation coefficient of 0.7577800 and p < 0.01 that emerged from our analysis signified a robust and statistically significant relationship between the Super Bowl point difference and the rating of "Two and a Half Men." This revelation left us both baffled and exhilarated, akin to the mix of emotions experienced by fans watching a game-deciding field goal attempt in the dying seconds of a Super Bowl showdown.
As we navigate through the labyrinthine world of data analysis, we are reminded of the words of acclaimed statistician, George Box, who famously said, "All models are wrong, but some are useful." Our findings, while unexpected, carry the potential to illuminate unconventional patterns lurking within the annals of data, perhaps serving as a reminder that correlations, like Super Bowl commercials, may hide in plain sight while bearing unexpected relevance.
Therefore, with the spirit of curiosity and a touch of lightheartedness, we invite our esteemed readers to join us in unraveling this delightful conundrum and to partake in the whimsical journey of deciphering the "Two and a Half Men" rating mystery in light of the grand spectacle that is the Super Bowl.
So, buckle up and prepare to tackle this puzzling connection with the same fervor as a team fighting for every yard on the gridiron, as we embark on a quest to fathom the quirkiness that binds the Super Bowl point difference and the rating of "Two and a Half Men" in delightful statistical harmony.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To tackle the perplexing conundrum of the interplay between the Super Bowl point difference and the rating of "Two and a Half Men," our research team embarked on a quest that would rival the intensity of a last-minute Hail Mary pass. Our data collection process resembled a cook-off, with a dash of determination, a sprinkle of skepticism, and a generous serving of internet browsing through Wikipedia and Nielsen ratings from 2004 to 2015.

First, to gauge the success of Super Bowl teams, we diligently scoured the internet for comprehensive point difference data from the games during the specified timeframe. As the saying goes, "research is 99% Wikipedia and 1% serendipity," our team harnessed the collective wisdom of the online encyclopedia and other reputable sources to amass a trove of point difference statistics.

Similarly, in the realm of television ratings, we harnessed the power of Nielsen ratings to capture the numerical essence of "Two and a Half Men's" performance. With the air of seasoned detectives, we delved into the labyrinthine world of sitcom ratings, unearthing the numerical representation of the show's triumphs and tribulations over the years.

With our data sources in tow, we then performed a rigorous statistical analysis that would make even the most meticulous accountants nod in approval. Armed with the calculating prowess of spreadsheet software, we computed the correlation coefficient between the Super Bowl point difference and the rating of "Two and a Half Men." It was a journey filled with suspense, akin to waiting for the referee's ruling on a game-deciding call – but with a touch of humor thrown in, much like a sitcom punchline.

Our methodology danced between the precision of statistical techniques and the whimsy of unexpected data patterns, not unlike a quarterback evading the clutches of a relentless defensive line. Ultimately, our findings emerged like a well-timed comedic punchline, shedding light on the improbable connection between the nail-biting drama of Super Bowl games and the comedic escapades of the "Two and a Half Men" cast.

In summary, as we embarked on this exploration of statistical serendipity, we combined the rigor of data analysis with a lighthearted appreciation for the peculiar and the unexpected. So come, dear reader, join us in this delightful statistical waltz as we unravel the enigmatic bond between Super Bowl point differences and the enduring legacy of "Two and a Half Men" ratings.

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 Super Bowl point difference and Season rating of "Two and a Half Men". Make lots of 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 Wikipedia and Wikipedia .

For the time period 2004 to 2015, you found a correlation 0.7577800, r-squared of 0.5742306, 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]]
Pointing to Success: The Super Bowl Point Difference and the Rating of "Two and a Half Men"

[[ABSTRACT]]
In this paper, we delve into the perplexing relationship between the point difference in Super Bowl games and the rating of the hit sitcom "Two and a Half Men." With punts, we mean points, and touchdowns, we aim to make a touchdown in understanding this quirky connection. Utilizing data from Wikipedia and Nielsen ratings, our research team has uncovered a statistically significant correlation coefficient of 0.7577800 and p < 0.01, spanning the years 2004 to 2015. Our findings suggest that there may be a peculiar link between the thrilling highs and disappointing lows of Super Bowl victories and the ratings of a sitcom filled with its own brand of comedic ups and downs. This study not only sheds light on an unexpected correlation but also shows the potential for quirky patterns in seemingly unrelated data. So, in the spirit of the Super Bowl, we invite readers to join us in this game of deciphering the "Two and a Half Men" rating mystery!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Introduction
In the realm of statistical analysis, one often encounters intriguing correlations that defy logical explanation at first glance. The connection between the point difference in Super Bowl games and the rating of the popular sitcom "Two and a Half Men" is one such enigmatic pairing that has piqued the curiosity of researchers and armchair statisticians alike. While the Super Bowl and a comedy series may seem as unrelated as a quarterback and a ballet dancer, our investigation has revealed a surprising relationship that calls for scrutiny.
The Super Bowl, with its heart-stopping plays and nail-biting finishes, captures the attention of millions, much like a captivating sitcom. The ebb and flow of excitement during the game mirrors the comedic highs and lows that "Two and a Half Men" delivers to its audience. It is within this realm of unpredictability and drama that we sought to unravel the puzzle of how the performance of football teams in the Super Bowl may manifest in the ratings of a television show centered around the comedic antics of Charlie Harper, Alan Harper, and Jake Harper.
Our research team combed through a trove of data from Wikipedia and Nielsen ratings, employing rigorous statistical analysis to uncover any semblance of a connection between the two seemingly disparate entities. Our pursuit led us down a path filled with unexpected twists and turns, akin to a thrilling Super Bowl matchup or a convoluted sitcom plot.
The correlation coefficient of 0.7577800 and p < 0.01 that emerged from our analysis signified a robust and statistically significant relationship between the Super Bowl point difference and the rating of "Two and a Half Men." This revelation left us both baffled and exhilarated, akin to the mix of emotions experienced by fans watching a game-deciding field goal attempt in the dying seconds of a Super Bowl showdown.
As we navigate through the labyrinthine world of data analysis, we are reminded of the words of acclaimed statistician, George Box, who famously said, "All models are wrong, but some are useful." Our findings, while unexpected, carry the potential to illuminate unconventional patterns lurking within the annals of data, perhaps serving as a reminder that correlations, like Super Bowl commercials, may hide in plain sight while bearing unexpected relevance.
Therefore, with the spirit of curiosity and a touch of lightheartedness, we invite our esteemed readers to join us in unraveling this delightful conundrum and to partake in the whimsical journey of deciphering the "Two and a Half Men" rating mystery in light of the grand spectacle that is the Super Bowl.
So, buckle up and prepare to tackle this puzzling connection with the same fervor as a team fighting for every yard on the gridiron, as we embark on a quest to fathom the quirkiness that binds the Super Bowl point difference and the rating of "Two and a Half Men" in delightful statistical harmony.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The analysis of data ranging from 2004 to 2015 revealed a striking correlation between the point difference in Super Bowl games and the rating of the sitcom "Two and a Half Men." Indeed, our research team uncovered a correlation coefficient of 0.7577800, an r-squared value of 0.5742306, and, much to our amazement, a p-value of less than 0.01. These statistical findings indicate a strong and significant relationship between these seemingly incongruent phenomena, leaving us both flabbergasted and gleeful, much like Charlie Harper stumbling into an unexpected windfall.

As shown in Fig. 1, the scatterplot vividly illustrates the compelling relationship between the two variables. This plot serves as a visual testament to the undeniable bond between the exhilarating triumphs and heartbreaking defeats of Super Bowl matchups and the audience appeal of a sitcom that has weathered its own share of triumphs and tribulations over the years.

In the peculiar world of statistical analysis, where one expects the unexpected, our results add a touch of whimsy to the otherwise serious arena of academic inquiry. The discovery of this unexpected correlation beckons to mind the unlikeliest of pairs harmonizing in perfect statistical sync, akin to a jazz duo comprised of an eager dung beetle and a maestro conductor.

While our findings may initially raise eyebrows, they open the door to a new realm of inquiry into the obscure and idiosyncratic connections that lie beneath the surface of seemingly unrelated phenomena. This unearthing serves as a friendly reminder that in the vast landscape of data analysis, surprising correlations may be lurking in the shadows, waiting to spring forth like a sitcom plot twist.

In summary, our research brings to light an unanticipated and statistically significant correlation between the point difference in Super Bowl games and the rating of "Two and a Half Men." This discovery not only piques academic curiosity but also invites us to ponder the whimsical and unpredictable nature of statistical relationships, much like the unpredictable dynamics of a quirky sitcom.

So, as we conclude this chapter of the academic symphony, we extend an invitation to our esteemed readers to join us in celebrating the delightfully peculiar bond that ties the ebbs and flows of Super Bowl victories to the ratings of "Two and a Half Men." Let us embrace this whimsical journey of deciphering the "Two and a Half Men" rating mystery, much like an enthusiastic fan cheering on a last-minute field goal attempt in a game-deciding Super Bowl showdown.

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 Super Bowl point difference and Season rating of "Two and a Half Men". Make lots of 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]]
Pointing to Success: The Super Bowl Point Difference and the Rating of "Two and a Half Men"

[[ABSTRACT]]
In this paper, we delve into the perplexing relationship between the point difference in Super Bowl games and the rating of the hit sitcom "Two and a Half Men." With punts, we mean points, and touchdowns, we aim to make a touchdown in understanding this quirky connection. Utilizing data from Wikipedia and Nielsen ratings, our research team has uncovered a statistically significant correlation coefficient of 0.7577800 and p < 0.01, spanning the years 2004 to 2015. Our findings suggest that there may be a peculiar link between the thrilling highs and disappointing lows of Super Bowl victories and the ratings of a sitcom filled with its own brand of comedic ups and downs. This study not only sheds light on an unexpected correlation but also shows the potential for quirky patterns in seemingly unrelated data. So, in the spirit of the Super Bowl, we invite readers to join us in this game of deciphering the "Two and a Half Men" rating mystery!

[[LITERATURE REVIEW]]
LITERATURE REVIEW
In "Smith and Doe (2010)," the authors find an unexpected correlation between Super Bowl point differences and television ratings of popular sitcoms. The study provides a comprehensive analysis of various sports events and their impact on entertainment viewership. Similarly, "Jones et al. (2012)" delve into the viewership patterns of comedic television series in relation to major sporting events, including the Super Bowl, revealing intriguing connections that challenge conventional wisdom.
Moving beyond the realm of academic studies, non-fiction books such as "Freakonomics" by Steven D. Levitt and Stephen J. Dubner, and "Outliers" by Malcolm Gladwell, offer insights into the intricate web of correlations and the often perplexing relationships that underpin seemingly unrelated phenomena. These scholarly works nimbly navigate through the labyrinth of statistics and human behavior, shedding light on unexpected linkages that titillate the mind and tickle the funny bone.
Further expanding the literary landscape, fictional works such as "Moneyball" by Michael Lewis and "The Art of Fielding" by Chad Harbach, present captivating narratives that center around the dynamics of sports and the idiosyncrasies of human nature, offering a glimpse into the enthralling tapestry of interconnectedness and chance encounters.
And now, for the pièce de résistance of our literature review – our research team's groundbreaking investigation encompassing an eclectic array of sources, from scholarly journals to *checks notes* grocery store receipts and even the occasional fortune cookie message. Yes, dear reader, the pursuit of knowledge knows no bounds, and neither does the quest for the perfect punchline!
With our exploratory journey spanning the intellectual, the fantastical, and, dare I say, the comically absurd, we now shift our focus to unraveling the enigma that is the relationship between the Super Bowl point difference and the rating of "Two and a Half Men." Gird your loins, dear academics and aficionados of statistical whimsy, for we are about to embark on a rollercoaster of statistical analysis that will make even the most riveting Super Bowl showdown seem, dare I say, a tad predictable.
Let the games begin!

[[RESULTS]]
The analysis of data ranging from 2004 to 2015 revealed a striking correlation between the point difference in Super Bowl games and the rating of the sitcom "Two and a Half Men." Indeed, our research team uncovered a correlation coefficient of 0.7577800, an r-squared value of 0.5742306, and, much to our amazement, a p-value of less than 0.01. These statistical findings indicate a strong and significant relationship between these seemingly incongruent phenomena, leaving us both flabbergasted and gleeful, much like Charlie Harper stumbling into an unexpected windfall.
As shown in Fig. 1, the scatterplot vividly illustrates the compelling relationship between the two variables. This plot serves as a visual testament to the undeniable bond between the exhilarating triumphs and heartbreaking defeats of Super Bowl matchups and the audience appeal of a sitcom that has weathered its own share of triumphs and tribulations over the years.
In the peculiar world of statistical analysis, where one expects the unexpected, our results add a touch of whimsy to the otherwise serious arena of academic inquiry. The discovery of this unexpected correlation beckons to mind the unlikeliest of pairs harmonizing in perfect statistical sync, akin to a jazz duo comprised of an eager dung beetle and a maestro conductor.
While our findings may initially raise eyebrows, they open the door to a new realm of inquiry into the obscure and idiosyncratic connections that lie beneath the surface of seemingly unrelated phenomena. This unearthing serves as a friendly reminder that in the vast landscape of data analysis, surprising correlations may be lurking in the shadows, waiting to spring forth like a sitcom plot twist.
In summary, our research brings to light an unanticipated and statistically significant correlation between the point difference in Super Bowl games and the rating of "Two and a Half Men." This discovery not only piques academic curiosity but also invites us to ponder the whimsical and unpredictable nature of statistical relationships, much like the unpredictable dynamics of a quirky sitcom.
So, as we conclude this chapter of the academic symphony, we extend an invitation to our esteemed readers to join us in celebrating the delightfully peculiar bond that ties the ebbs and flows of Super Bowl victories to the ratings of "Two and a Half Men." Let us embrace this whimsical journey of deciphering the "Two and a Half Men" rating mystery, much like an enthusiastic fan cheering on a last-minute field goal attempt in a game-deciding Super Bowl showdown.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our investigation into the perplexing relationship between the point difference in Super Bowl games and the rating of "Two and a Half Men" has not only revealed a statistically significant correlation but has also ignited the flames of curiosity amidst the quirky intricacies of statistical analysis.

Harkening back to the scholarly works that deftly navigated the intertwining pathways of statistical phenomena and human behavior, our findings stand as a testament to the unforeseen connections that underpin seemingly unrelated events. As "Smith and Doe (2010)" and "Jones et al. (2012)" provocatively hinted at the unexpected correlations between major sporting events and entertainment viewership, our study provides empirical support for these audacious claims, akin to the exhilarating rush of a surprise onside kick.

In the spirit of embracing the unexpected, our statistical analysis not only corroborates but amplifies the intriguing parallels outlined in the literary landscape we passionately traversed. Leveraging the zany joys of non-fiction works such as "Freakonomics" and "Outliers," our findings toss a sprinkle of statistical confetti onto the already electrifying stage of unlikely correlations, much like a spontaneous touchdown celebration during a Super Bowl halftime show.

Furthermore, our data, gathered from a seemingly incongruous array of sources including grocery store receipts and *ahem* fortune cookie messages, speaks to the boundless nature of academic inquiry and the unyielding pursuit of knowledge. Our pioneering exploration into this correlation boldly echoes the essence of literature review oddities, inviting readers to join us in this game of deciphering statistical whimsy like a group of academics engaged in a heated game of statistical charades.

As we meander through the whimsical landscape of unpredictable statistical relationships, our findings beckon to the lighthearted musings and comical absurdities that characterize the peculiar world of academia. Our study not only adds a touch of statistical whimsy to the serious discourse of academic inquiry but also prompts curiosity to bloom, much like a carefully tended garden reaching the crescendo of bloom amidst the unpredictable climate of statistical inquiry.

In essence, our research contributes to the scholarly mosaic by unraveling the unexpected rapport between the intensity of Super Bowl victories and the audience appeal of "Two and a Half Men." We invite our esteemed readers to revel in the joy of deciphering this quirky correlation, imbued with the spirit of statistical whimsy and academic camaraderie, much like a lively tailgate party preceding a thrilling Super Bowl showdown.

Let us continue this playfully profound adventure into deciphering the enigmatic "Two and a Half Men" rating mystery, celebrating the delightful fusion of statistical caprice and academic inquiry, much like a perplexing yet delightful cliffhanger in a beloved sitcom.

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 Super Bowl point difference and Season rating of "Two and a Half Men". Make lots of 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]]
Pointing to Success: The Super Bowl Point Difference and the Rating of "Two and a Half Men"

[[ABSTRACT]]
In this paper, we delve into the perplexing relationship between the point difference in Super Bowl games and the rating of the hit sitcom "Two and a Half Men." With punts, we mean points, and touchdowns, we aim to make a touchdown in understanding this quirky connection. Utilizing data from Wikipedia and Nielsen ratings, our research team has uncovered a statistically significant correlation coefficient of 0.7577800 and p < 0.01, spanning the years 2004 to 2015. Our findings suggest that there may be a peculiar link between the thrilling highs and disappointing lows of Super Bowl victories and the ratings of a sitcom filled with its own brand of comedic ups and downs. This study not only sheds light on an unexpected correlation but also shows the potential for quirky patterns in seemingly unrelated data. So, in the spirit of the Super Bowl, we invite readers to join us in this game of deciphering the "Two and a Half Men" rating mystery!

[[INTRDUCTION]]
Introduction
In the realm of statistical analysis, one often encounters intriguing correlations that defy logical explanation at first glance. The connection between the point difference in Super Bowl games and the rating of the popular sitcom "Two and a Half Men" is one such enigmatic pairing that has piqued the curiosity of researchers and armchair statisticians alike. While the Super Bowl and a comedy series may seem as unrelated as a quarterback and a ballet dancer, our investigation has revealed a surprising relationship that calls for scrutiny.
The Super Bowl, with its heart-stopping plays and nail-biting finishes, captures the attention of millions, much like a captivating sitcom. The ebb and flow of excitement during the game mirrors the comedic highs and lows that "Two and a Half Men" delivers to its audience. It is within this realm of unpredictability and drama that we sought to unravel the puzzle of how the performance of football teams in the Super Bowl may manifest in the ratings of a television show centered around the comedic antics of Charlie Harper, Alan Harper, and Jake Harper.
Our research team combed through a trove of data from Wikipedia and Nielsen ratings, employing rigorous statistical analysis to uncover any semblance of a connection between the two seemingly disparate entities. Our pursuit led us down a path filled with unexpected twists and turns, akin to a thrilling Super Bowl matchup or a convoluted sitcom plot.
The correlation coefficient of 0.7577800 and p < 0.01 that emerged from our analysis signified a robust and statistically significant relationship between the Super Bowl point difference and the rating of "Two and a Half Men." This revelation left us both baffled and exhilarated, akin to the mix of emotions experienced by fans watching a game-deciding field goal attempt in the dying seconds of a Super Bowl showdown.
As we navigate through the labyrinthine world of data analysis, we are reminded of the words of acclaimed statistician, George Box, who famously said, "All models are wrong, but some are useful." Our findings, while unexpected, carry the potential to illuminate unconventional patterns lurking within the annals of data, perhaps serving as a reminder that correlations, like Super Bowl commercials, may hide in plain sight while bearing unexpected relevance.
Therefore, with the spirit of curiosity and a touch of lightheartedness, we invite our esteemed readers to join us in unraveling this delightful conundrum and to partake in the whimsical journey of deciphering the "Two and a Half Men" rating mystery in light of the grand spectacle that is the Super Bowl.
So, buckle up and prepare to tackle this puzzling connection with the same fervor as a team fighting for every yard on the gridiron, as we embark on a quest to fathom the quirkiness that binds the Super Bowl point difference and the rating of "Two and a Half Men" in delightful statistical harmony.

[[RESULTS]]
The analysis of data ranging from 2004 to 2015 revealed a striking correlation between the point difference in Super Bowl games and the rating of the sitcom "Two and a Half Men." Indeed, our research team uncovered a correlation coefficient of 0.7577800, an r-squared value of 0.5742306, and, much to our amazement, a p-value of less than 0.01. These statistical findings indicate a strong and significant relationship between these seemingly incongruent phenomena, leaving us both flabbergasted and gleeful, much like Charlie Harper stumbling into an unexpected windfall.
As shown in Fig. 1, the scatterplot vividly illustrates the compelling relationship between the two variables. This plot serves as a visual testament to the undeniable bond between the exhilarating triumphs and heartbreaking defeats of Super Bowl matchups and the audience appeal of a sitcom that has weathered its own share of triumphs and tribulations over the years.
In the peculiar world of statistical analysis, where one expects the unexpected, our results add a touch of whimsy to the otherwise serious arena of academic inquiry. The discovery of this unexpected correlation beckons to mind the unlikeliest of pairs harmonizing in perfect statistical sync, akin to a jazz duo comprised of an eager dung beetle and a maestro conductor.
While our findings may initially raise eyebrows, they open the door to a new realm of inquiry into the obscure and idiosyncratic connections that lie beneath the surface of seemingly unrelated phenomena. This unearthing serves as a friendly reminder that in the vast landscape of data analysis, surprising correlations may be lurking in the shadows, waiting to spring forth like a sitcom plot twist.
In summary, our research brings to light an unanticipated and statistically significant correlation between the point difference in Super Bowl games and the rating of "Two and a Half Men." This discovery not only piques academic curiosity but also invites us to ponder the whimsical and unpredictable nature of statistical relationships, much like the unpredictable dynamics of a quirky sitcom.
So, as we conclude this chapter of the academic symphony, we extend an invitation to our esteemed readers to join us in celebrating the delightfully peculiar bond that ties the ebbs and flows of Super Bowl victories to the ratings of "Two and a Half Men." Let us embrace this whimsical journey of deciphering the "Two and a Half Men" rating mystery, much like an enthusiastic fan cheering on a last-minute field goal attempt in a game-deciding Super Bowl showdown.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has highlighted the unexpected connection between the thrilling rollercoaster of Super Bowl point differences and the ratings of "Two and a Half Men", much like finding a hidden punchline in a sitcom script. The statistically significant correlation coefficient of 0.7577800 and p < 0.01 has left us as astonished as a character realizing they're living in a sitcom. Our findings underscore the whimsical nature of statistical relationships, proving that even in the serious world of academic research, there's always room for a dash of lighthearted quirkiness.

While our results may seem as surprising as a sitcom plot twist, they emphasize the potential for uncovering unlikely correlations between seemingly unrelated variables. Our study stands as a testament to the playful spirit of inquiry, reminding us that beneath the layers of data lie hidden jokes waiting to be discovered, much like an Easter egg hunt during a commercial break.

As for the need for further research, we assert with the utmost seriousness that no further study is required in this area. We have undoubtedly reached the pinnacle of statistical whimsy in uncovering the link between the Super Bowl's highs and lows and the ratings of "Two and a Half Men." It's time to take the field and celebrate the touchdown of discovery, leaving no yardage for additional investigation in this particular realm of statistical comedy.

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 Pointing to Success: The Super Bowl Point Difference and the Rating of Two and a Half Men. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Sports and Pop Culture Analysis

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Pointing to Success: The Super Bowl Point Difference and the Rating of Two and a Half Men. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Interdisciplinary Research in Entertainment and Sports Analytics

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 Pointing to Success: The Super Bowl Point Difference and the Rating of Two and a Half Men with an abstract of In this paper, we delve into the perplexing relationship between the point difference in Super Bowl games and the rating of the hit sitcom "Two and a Half Men." With punts, we mean points, and touchdowns, we aim to make a touchdown in understanding this quirky connection. Utilizing data from Wikipedia and Nielsen ratings, our research team has uncovered a statistically significant correlation coefficient of 0.7577800 and p < 0.01, spanning the years 2004 to 2015. Our findings suggest that there may be a peculiar link between the thrilling highs and disappointing lows of Super Bowl victories and the ratings of a sitcom filled with its own brand of comedic ups and downs. This study not only sheds light on an unexpected correlation but also shows the potential for quirky patterns in seemingly unrelated data. So, in the spirit of the Super Bowl, we invite readers to join us in this game of deciphering the "Two and a Half Men" rating mystery!

ChatGPT:

Super Bowl, point difference, Super Bowl games, rating, Two and a Half Men, correlation, statistical significance, Nielsen ratings, Wikipedia, quirky patterns, sitcom rating, correlation coefficient, data analysis

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

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Data details

Super Bowl point difference
Source: Wikipedia
See what else correlates with Super Bowl point difference

Season rating of "Two and a Half Men"
Source: Wikipedia
See what else correlates with Season rating of "Two and a Half Men"

Correlation r = 0.7577800 (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 = 0.5742306 (Coefficient of determination)
This means 57.4% of the change in the one variable (i.e., Season rating of "Two and a Half Men") is predictable based on the change in the other (i.e., Super Bowl point difference) over the 12 years from 2004 through 2015.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0043.^Show 0.0042996717472844614000000000
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 0.76 in 0.43% of random cases. Said differently, if you correlated 233 random variables^Note Which I absolutely did.
with the same 11 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 11 because we have two variables measured over a period of 12 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.

[ 0.33, 0.93 ] 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.

	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
Super Bowl point difference (Points)	3	3	11	12	3	4	14	6	4	3	35	14
Season rating of "Two and a Half Men" (Rating)	15	11	17	21	17	10	11	17	11	11	27	23

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^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.

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([3,3,11,12,3,4,14,6,4,3,35,14,])
array_2 = np.array([15,11,17,21,17,10,11,17,11,11,27,23,])
array_1_name = "Super Bowl point difference"
array_2_name = "Season rating of "Two and a Half Men""

# 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)
Portable line chart (png), optimized for mobile
Line chart for only Super Bowl point difference
Line chart for only Season rating of "Two and a Half Men"
AI-generated correlation image
The spurious research paper: Pointing to Success: The Super Bowl Point Difference and the Rating of Two and a Half Men

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Correlation ID: 1678 · Black Variable ID: 541 · Red Variable ID: 147


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