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Spurious correlation #3,622 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Annual US household spending on electricity and the second variable is Points scored by the Dallas Cowboys.  The chart goes from 2000 to 2022, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As household spending on electricity went up, more fans were able to power their lucky Cowboys gear, leading to an increase in team spirit. This surge in positive energy electrified the players, sparking a boost in their performance on the field.

Model: dalle-3
Prompt: Generate an image of a photorealistic scene where the Dallas Cowboys are depicted celebrating a triumphant moment in a brightly lit stadium. Their well-lit surroundings should highlight their powerful kick as they send the football soaring through the goalposts. The stadium lights should cast a radiant glow, symbolizing the increase in annual US household spending on electricity, and accentuating the team's victorious achievement. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.
This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Annual US household spending on electricity and Points scored by the Dallas Cowboys. 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: Steampunk Cutsy graphics Brutalism Photorealism inspired by bauhaus
ChatGPT: Photorealism

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Annual US household spending on electricity and Points scored by the Dallas Cowboys, in the following situation: Because the increase in annual US household spending on electricity led to brighter stadium lights, the Dallas Cowboys were able to see the goalposts more clearly, resulting in improved accuracy and higher scoring. This sheds a new light on their performance, illuminating their path to victory.. Keep the description simple.
ChatGPT: In a brightly lit stadium, the Dallas Cowboys kick the football through the goalposts with precision, scoring more points than ever before. The glow of the stadium lights reflects the increase in annual US household spending on electricity, illuminating the path to their victory.

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: [ Photorealism ] and depicting this scene: In a brightly lit stadium, the Dallas Cowboys kick the football through the goalposts with precision, scoring more points than ever before. The glow of the stadium lights reflects the increase in annual US household spending on electricity, illuminating the path to their victory. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a photorealistic scene where the Dallas Cowboys are depicted celebrating a triumphant moment in a brightly lit stadium. Their well-lit surroundings should highlight their powerful kick as they send the football soaring through the goalposts. The stadium lights should cast a radiant glow, symbolizing the increase in annual US household spending on electricity, and accentuating the team's victorious achievement.

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

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

Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Annual US household spending on electricity caused Points scored by the Dallas Cowboys to increase.

AI academic paper

(Because p < 0.01)
Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation

The Journal of Witty Science

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 Annual US household spending on electricity and Points scored by the Dallas Cowboys. Make lots of jokes, goofy observations, and puns.

Make the title an alliteration.

Your research team used data from Bureau of Labor Statistics and Pro-Football-Reference.com to assess this nagging question. You found a correlation coefficient of 0.7373286 and p < 0.01 for 2000 to 2022.

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]]
Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation

[[ABSTRACT]]
This study delves into the peculiar phenomenon of the relationship between annual US household spending on electricity and the points scored by the Dallas Cowboys, bridging the disparate realms of household economics and gridiron glory. Leveraging data from the Bureau of Labor Statistics and Pro-Football-Reference.com, our research team scrutinized the perplexing nexus between power bills and pigskin prowess. In a surprising twist worthy of a dramatic game-winning touchdown, we unearthed a robust correlation coefficient of 0.7373286 and a statistically significant p-value of less than 0.01 for the period spanning from 2000 to 2022. Our findings suggest that there may well be an electrifying connection between the flicker of household lights and the flick of a football across the goal line -- a delightful discovery that may leave skeptics and statisticians alike scratching their heads in bemusement.


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 Annual US household spending on electricity and Points scored by the Dallas Cowboys. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation

[[ABSTRACT]]
This study delves into the peculiar phenomenon of the relationship between annual US household spending on electricity and the points scored by the Dallas Cowboys, bridging the disparate realms of household economics and gridiron glory. Leveraging data from the Bureau of Labor Statistics and Pro-Football-Reference.com, our research team scrutinized the perplexing nexus between power bills and pigskin prowess. In a surprising twist worthy of a dramatic game-winning touchdown, we unearthed a robust correlation coefficient of 0.7373286 and a statistically significant p-value of less than 0.01 for the period spanning from 2000 to 2022. Our findings suggest that there may well be an electrifying connection between the flicker of household lights and the flick of a football across the goal line -- a delightful discovery that may leave skeptics and statisticians alike scratching their heads in bemusement.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

Behold, dear readers, as we embark on an electrifying journey into the whimsical world of statistics and sports – an odyssey that unveils the seemingly incongruous link between the annual US household spending on electricity and the points scored by none other than the illustrious Dallas Cowboys. This peculiar pairing of variables presents rich fodder for investigation, conjuring a curious confluence of realms that straddle the domestic economy and the grand stage of the gridiron.

As we delve into this comical correlation, it is prudent to acknowledge the sheer audacity of such an undertaking. Who would envisage that the flicker of a light bulb in Omaha could hold any sway over the fate of a touchdown in Dallas? A skeptic may scoff at the very notion, and a stringent statistician may raise an eyebrow in incredulity. Yet, in the unfathomable depths of data lay intriguing patterns waiting to emerge, and it is with a mix of prudence and jest that we wade into this enigmatic sea of numbers.

Our noble quest is propelled by an unquenchable thirst for uncovering the hidden threads that weave through the fabric of our existence – or in the whimsical world of data analysis, the covariation that binds disparate variables. The very notion of household energy expenses influencing the performance of a storied football franchise may provoke a chuckle or two, but the pursuit of knowledge knows no bounds, not even the boundaries of conventional wisdom.

In this investigator's paradise, we have harnessed the power of quantitative analysis, let loose the p-value pirates, and marshaled the forces of regression to discern whether there exists a correlation between household electricity spending and the efficacy of field goals. The scientific method stands as our guiding lodestar, although a dash of humor and a sprinkle of mirth shall never be amiss as we navigate this unorthodox territory.

So, ready your calculators, keep an eye on that p-value, and prepare to revel in the delightful absurdity of our findings. For as we peer deep into the heart of this statistical tapestry, we may find that the hum of the microwave and the hum of the crowd are not as distant as once presumed. The game of correlations awaits, and the wisdom that we uncover from this whimsical inquiry may well light the path to future revelations – or, at the very least, provide amusement for the discerning reader.


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 Annual US household spending on electricity and Points scored by the Dallas Cowboys. 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]]
Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation

[[ABSTRACT]]
This study delves into the peculiar phenomenon of the relationship between annual US household spending on electricity and the points scored by the Dallas Cowboys, bridging the disparate realms of household economics and gridiron glory. Leveraging data from the Bureau of Labor Statistics and Pro-Football-Reference.com, our research team scrutinized the perplexing nexus between power bills and pigskin prowess. In a surprising twist worthy of a dramatic game-winning touchdown, we unearthed a robust correlation coefficient of 0.7373286 and a statistically significant p-value of less than 0.01 for the period spanning from 2000 to 2022. Our findings suggest that there may well be an electrifying connection between the flicker of household lights and the flick of a football across the goal line -- a delightful discovery that may leave skeptics and statisticians alike scratching their heads in bemusement.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Behold, dear readers, as we embark on an electrifying journey into the whimsical world of statistics and sports – an odyssey that unveils the seemingly incongruous link between the annual US household spending on electricity and the points scored by none other than the illustrious Dallas Cowboys. This peculiar pairing of variables presents rich fodder for investigation, conjuring a curious confluence of realms that straddle the domestic economy and the grand stage of the gridiron.
As we delve into this comical correlation, it is prudent to acknowledge the sheer audacity of such an undertaking. Who would envisage that the flicker of a light bulb in Omaha could hold any sway over the fate of a touchdown in Dallas? A skeptic may scoff at the very notion, and a stringent statistician may raise an eyebrow in incredulity. Yet, in the unfathomable depths of data lay intriguing patterns waiting to emerge, and it is with a mix of prudence and jest that we wade into this enigmatic sea of numbers.
Our noble quest is propelled by an unquenchable thirst for uncovering the hidden threads that weave through the fabric of our existence – or in the whimsical world of data analysis, the covariation that binds disparate variables. The very notion of household energy expenses influencing the performance of a storied football franchise may provoke a chuckle or two, but the pursuit of knowledge knows no bounds, not even the boundaries of conventional wisdom.
In this investigator's paradise, we have harnessed the power of quantitative analysis, let loose the p-value pirates, and marshaled the forces of regression to discern whether there exists a correlation between household electricity spending and the efficacy of field goals. The scientific method stands as our guiding lodestar, although a dash of humor and a sprinkle of mirth shall never be amiss as we navigate this unorthodox territory.
So, ready your calculators, keep an eye on that p-value, and prepare to revel in the delightful absurdity of our findings. For as we peer deep into the heart of this statistical tapestry, we may find that the hum of the microwave and the hum of the crowd are not as distant as once presumed. The game of correlations awaits, and the wisdom that we uncover from this whimsical inquiry may well light the path to future revelations – or, at the very least, provide amusement for the discerning reader.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The obscure relationship between annual US household spending on electricity and the performance of the Dallas Cowboys has long captivated the imaginations of researchers and sports enthusiasts alike. While initial inquiries into this correlation seemed as outlandish as a touchdown pass thrown by a polar bear, recent scholarship has unearthed surprising insights, akin to a perfectly executed onside kick.

In "Smith and Doe's Household Economics: A Practical Analysis," the authors find that expenditures on utilities, including electricity, are not typically associated with sports team performance. However, upon deeper scrutiny, it becomes apparent that the whimsy of this correlation transcends the drudgery of mere utility bills. In "Jones's Gridiron Gazette: Musings on Sports, Statistics, and Shenanigans," the authors deftly explore the convoluted world of sports analytics, raising the tantalizing prospect that the throes of spectating a football game may, in fact, sway consumer behavior.

Turning to non-fiction literature, Lawrence S. Ritter's "The Glory of Their Times" and Michael Lewis's "Moneyball" both offer valuable insights into the enigmatic realms of sports economics and statistical analysis. However, it is the fictional works of Dan Jenkins's "Semi-Tough" and Tom Robinson's "Hard-Headed Fella" that hint at the whimsical connections between household expenditures and athletic prowess, albeit in a cheeky and fictional context.

Departing from the realms of conventional scholarship, the authors admit, with a tongue planted firmly in cheek, to have scoured the unconventional sources for clues on this comical correlation. From perusing the backs of breakfast cereal boxes to gently caressing the labels of shampoo bottles in moments of dire scholarly exhaustion, the research team left no stone unturned in this pursuit. While the efficacy of such methods remains dubious, the absurdity of our undertaking has not escaped us, as we embrace the delightful tango between the quotidian and the utterly preposterous.


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 Annual US household spending on electricity and Points scored by the Dallas Cowboys. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Labor Statistics and Pro-Football-Reference.com . You used data from 2000 to 2022

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation

[[ABSTRACT]]
This study delves into the peculiar phenomenon of the relationship between annual US household spending on electricity and the points scored by the Dallas Cowboys, bridging the disparate realms of household economics and gridiron glory. Leveraging data from the Bureau of Labor Statistics and Pro-Football-Reference.com, our research team scrutinized the perplexing nexus between power bills and pigskin prowess. In a surprising twist worthy of a dramatic game-winning touchdown, we unearthed a robust correlation coefficient of 0.7373286 and a statistically significant p-value of less than 0.01 for the period spanning from 2000 to 2022. Our findings suggest that there may well be an electrifying connection between the flicker of household lights and the flick of a football across the goal line -- a delightful discovery that may leave skeptics and statisticians alike scratching their heads in bemusement.

[[INTRODUCTION]]
Behold, dear readers, as we embark on an electrifying journey into the whimsical world of statistics and sports – an odyssey that unveils the seemingly incongruous link between the annual US household spending on electricity and the points scored by none other than the illustrious Dallas Cowboys. This peculiar pairing of variables presents rich fodder for investigation, conjuring a curious confluence of realms that straddle the domestic economy and the grand stage of the gridiron.
As we delve into this comical correlation, it is prudent to acknowledge the sheer audacity of such an undertaking. Who would envisage that the flicker of a light bulb in Omaha could hold any sway over the fate of a touchdown in Dallas? A skeptic may scoff at the very notion, and a stringent statistician may raise an eyebrow in incredulity. Yet, in the unfathomable depths of data lay intriguing patterns waiting to emerge, and it is with a mix of prudence and jest that we wade into this enigmatic sea of numbers.
Our noble quest is propelled by an unquenchable thirst for uncovering the hidden threads that weave through the fabric of our existence – or in the whimsical world of data analysis, the covariation that binds disparate variables. The very notion of household energy expenses influencing the performance of a storied football franchise may provoke a chuckle or two, but the pursuit of knowledge knows no bounds, not even the boundaries of conventional wisdom.
In this investigator's paradise, we have harnessed the power of quantitative analysis, let loose the p-value pirates, and marshaled the forces of regression to discern whether there exists a correlation between household electricity spending and the efficacy of field goals. The scientific method stands as our guiding lodestar, although a dash of humor and a sprinkle of mirth shall never be amiss as we navigate this unorthodox territory.
So, ready your calculators, keep an eye on that p-value, and prepare to revel in the delightful absurdity of our findings. For as we peer deep into the heart of this statistical tapestry, we may find that the hum of the microwave and the hum of the crowd are not as distant as once presumed. The game of correlations awaits, and the wisdom that we uncover from this whimsical inquiry may well light the path to future revelations – or, at the very least, provide amusement for the discerning reader.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study was as eclectic as the curious correlation we sought to explore. Our research endeavor harnessed the power of both household expenditure data and football performance metrics, intertwining them in a statistical tango that would make even the most agile of quarterbacks envious.

First and foremost, we scoured the digital expanse to collect data on annual US household spending on electricity from the esteemed Bureau of Labor Statistics. With a few keystrokes and clicks, we unearthed a treasure trove of household expenditure data, showcasing the financial ebbs and flows of American homes in response to the electrifying demands of modern existence. From the flicker of a bedside lamp during late-night reading sessions to the hum of a refrigerator housing delectable snacks for game day, every watt of energy expenditure was meticulously accounted for.

Simultaneously, we delved into the hallowed annals of Pro-Football-Reference.com, where the legendary exploits of the Dallas Cowboys were enshrined in digital glory. With the grace of a lineman executing a flawless block, we extracted the points scored by this iconic franchise over the same period, uncovering the thrilling narrative of touchdowns, field goals, and game-winning heroics.

Once the data sets were secured, we engaged in a boisterous dance with mathematical models. With the incomparable power of statistical software at our fingertips, we loosed the forces of regression analysis to untangle the web of covariation between household electricity spending and the gridiron prowess of the Cowboys. Like a conductor directing a symphony, we commanded covariate adjustment and scrutinized goodness-of-fit measures to ensure a robust analysis worthy of our whimsical inquiry.

Having meticulously prepared our data and performed elaborate statistical maneuvers, we summoned the p-value pirates to gauge the significance of our findings. English majors may speak of "poetic license," but in our statistical escapade, the p-value wielded its own peculiar brand of authority, leading us to the tantalizing revelation of a robust correlation coefficient and a p-value of less than 0.01.

With a hearty dose of humor and a twinkle in our eyes, we harnessed the power of data visualization to imbue our findings with a narrative flair that would be the envy of any bard. Through scatter plots and regression lines, we sought to bring to life the tale of electricity expenditure and the Cowboys' clout, hoping to elicit both applause and amusement from the discerning reader.

And thus, with a whimsical blend of data wrangling, statistical sorcery, and a generous dash of light-heartedness, our methodology led us to the delightful nexus where household economics and gridiron glory met in capricious camaraderie.


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 Annual US household spending on electricity and Points scored by the Dallas Cowboys. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Labor Statistics and Pro-Football-Reference.com .

For the time period 2000 to 2022, you found a correlation 0.7373286, r-squared of 0.5436535, 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]]
Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation

[[ABSTRACT]]
This study delves into the peculiar phenomenon of the relationship between annual US household spending on electricity and the points scored by the Dallas Cowboys, bridging the disparate realms of household economics and gridiron glory. Leveraging data from the Bureau of Labor Statistics and Pro-Football-Reference.com, our research team scrutinized the perplexing nexus between power bills and pigskin prowess. In a surprising twist worthy of a dramatic game-winning touchdown, we unearthed a robust correlation coefficient of 0.7373286 and a statistically significant p-value of less than 0.01 for the period spanning from 2000 to 2022. Our findings suggest that there may well be an electrifying connection between the flicker of household lights and the flick of a football across the goal line -- a delightful discovery that may leave skeptics and statisticians alike scratching their heads in bemusement.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Behold, dear readers, as we embark on an electrifying journey into the whimsical world of statistics and sports – an odyssey that unveils the seemingly incongruous link between the annual US household spending on electricity and the points scored by none other than the illustrious Dallas Cowboys. This peculiar pairing of variables presents rich fodder for investigation, conjuring a curious confluence of realms that straddle the domestic economy and the grand stage of the gridiron.
As we delve into this comical correlation, it is prudent to acknowledge the sheer audacity of such an undertaking. Who would envisage that the flicker of a light bulb in Omaha could hold any sway over the fate of a touchdown in Dallas? A skeptic may scoff at the very notion, and a stringent statistician may raise an eyebrow in incredulity. Yet, in the unfathomable depths of data lay intriguing patterns waiting to emerge, and it is with a mix of prudence and jest that we wade into this enigmatic sea of numbers.
Our noble quest is propelled by an unquenchable thirst for uncovering the hidden threads that weave through the fabric of our existence – or in the whimsical world of data analysis, the covariation that binds disparate variables. The very notion of household energy expenses influencing the performance of a storied football franchise may provoke a chuckle or two, but the pursuit of knowledge knows no bounds, not even the boundaries of conventional wisdom.
In this investigator's paradise, we have harnessed the power of quantitative analysis, let loose the p-value pirates, and marshaled the forces of regression to discern whether there exists a correlation between household electricity spending and the efficacy of field goals. The scientific method stands as our guiding lodestar, although a dash of humor and a sprinkle of mirth shall never be amiss as we navigate this unorthodox territory.
So, ready your calculators, keep an eye on that p-value, and prepare to revel in the delightful absurdity of our findings. For as we peer deep into the heart of this statistical tapestry, we may find that the hum of the microwave and the hum of the crowd are not as distant as once presumed. The game of correlations awaits, and the wisdom that we uncover from this whimsical inquiry may well light the path to future revelations – or, at the very least, provide amusement for the discerning reader.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

Our intrepid exploration into the zany world of statistics and sports has yielded some truly electrifying results. The correlation coefficient between annual US household spending on electricity and the points scored by the Dallas Cowboys from 2000 to 2022 stood at a shocking 0.7373286, accompanied by an r-squared value of 0.5436535. To top it off, the p-value struts in with flair, boasting a significance level of less than 0.01. It seems that we have stumbled upon a statistical touchdown here!

In Figure 1, dear readers, we present our pièce de résistance – a captivating scatterplot that vividly illustrates the striking correlation between these unlikely bedfellows. The data points dance gleefully across the chart, twirling in perfect synchrony, much like a well-coordinated touchdown drive.

It's fair to say that the correlation we uncovered is truly "current." With an r-squared value of 0.5436535, more than half of the variation in the points scored by the Dallas Cowboys can be explained by the variation in household electricity spending. We might amuse ourselves with the notion that, just like power surges dictate the ebb and flow of energy in a household, the Cowboys' triumphs and setbacks are intertwined with the vicissitudes of power consumption.

The correlation coefficient of 0.7373286 indicates a strong positive relationship between the two variables. One might quip that the connection between household electricity expenditure and the Cowboys' performance is as luminous as a floodlit football stadium on a crisp autumn evening. This finding tickles the fancy and raises an eyebrow, much like an unexpected fake punt in the fourth quarter.

Now, this statistical spectacle brings forth an amusing prospect: Could it be that a household's electricity bill has some mysterious influence over the fate of soaring field goals and dramatic end zone celebrations? Much like a sudden fake field goal attempt, our findings add a twist to the narrative of statistical associations and beckon us to ponder the whimsical juxtaposition of household dynamics and athletic prowess.

In a world where numbers and whimsy collide, our inquiry has delivered an unexpected touchdown of insight. The delightfully absurd correlation we uncovered between annual US household spending on electricity and the points scored by the Dallas Cowboys challenges conventional wisdom and may very well leave even the most seasoned statisticians bemused.

In summary, our findings suggest a compelling connection between the flicker of household lights and the flick of a football across the goal line. As we bask in the glow of this discovery, let's not forget to appreciate the statistical symphony that unfolds before us, where the unexpected harmonizes with the empirical.


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 Annual US household spending on electricity and Points scored by the Dallas Cowboys. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation

[[ABSTRACT]]
This study delves into the peculiar phenomenon of the relationship between annual US household spending on electricity and the points scored by the Dallas Cowboys, bridging the disparate realms of household economics and gridiron glory. Leveraging data from the Bureau of Labor Statistics and Pro-Football-Reference.com, our research team scrutinized the perplexing nexus between power bills and pigskin prowess. In a surprising twist worthy of a dramatic game-winning touchdown, we unearthed a robust correlation coefficient of 0.7373286 and a statistically significant p-value of less than 0.01 for the period spanning from 2000 to 2022. Our findings suggest that there may well be an electrifying connection between the flicker of household lights and the flick of a football across the goal line -- a delightful discovery that may leave skeptics and statisticians alike scratching their heads in bemusement.

[[LITERATURE REVIEW]]
The obscure relationship between annual US household spending on electricity and the performance of the Dallas Cowboys has long captivated the imaginations of researchers and sports enthusiasts alike. While initial inquiries into this correlation seemed as outlandish as a touchdown pass thrown by a polar bear, recent scholarship has unearthed surprising insights, akin to a perfectly executed onside kick.
In "Smith and Doe's Household Economics: A Practical Analysis," the authors find that expenditures on utilities, including electricity, are not typically associated with sports team performance. However, upon deeper scrutiny, it becomes apparent that the whimsy of this correlation transcends the drudgery of mere utility bills. In "Jones's Gridiron Gazette: Musings on Sports, Statistics, and Shenanigans," the authors deftly explore the convoluted world of sports analytics, raising the tantalizing prospect that the throes of spectating a football game may, in fact, sway consumer behavior.
Turning to non-fiction literature, Lawrence S. Ritter's "The Glory of Their Times" and Michael Lewis's "Moneyball" both offer valuable insights into the enigmatic realms of sports economics and statistical analysis. However, it is the fictional works of Dan Jenkins's "Semi-Tough" and Tom Robinson's "Hard-Headed Fella" that hint at the whimsical connections between household expenditures and athletic prowess, albeit in a cheeky and fictional context.
Departing from the realms of conventional scholarship, the authors admit, with a tongue planted firmly in cheek, to have scoured the unconventional sources for clues on this comical correlation. From perusing the backs of breakfast cereal boxes to gently caressing the labels of shampoo bottles in moments of dire scholarly exhaustion, the research team left no stone unturned in this pursuit. While the efficacy of such methods remains dubious, the absurdity of our undertaking has not escaped us, as we embrace the delightful tango between the quotidian and the utterly preposterous.

[[RESULTS]]
Our intrepid exploration into the zany world of statistics and sports has yielded some truly electrifying results. The correlation coefficient between annual US household spending on electricity and the points scored by the Dallas Cowboys from 2000 to 2022 stood at a shocking 0.7373286, accompanied by an r-squared value of 0.5436535. To top it off, the p-value struts in with flair, boasting a significance level of less than 0.01. It seems that we have stumbled upon a statistical touchdown here!
In Figure 1, dear readers, we present our pièce de résistance – a captivating scatterplot that vividly illustrates the striking correlation between these unlikely bedfellows. The data points dance gleefully across the chart, twirling in perfect synchrony, much like a well-coordinated touchdown drive.
It's fair to say that the correlation we uncovered is truly "current." With an r-squared value of 0.5436535, more than half of the variation in the points scored by the Dallas Cowboys can be explained by the variation in household electricity spending. We might amuse ourselves with the notion that, just like power surges dictate the ebb and flow of energy in a household, the Cowboys' triumphs and setbacks are intertwined with the vicissitudes of power consumption.
The correlation coefficient of 0.7373286 indicates a strong positive relationship between the two variables. One might quip that the connection between household electricity expenditure and the Cowboys' performance is as luminous as a floodlit football stadium on a crisp autumn evening. This finding tickles the fancy and raises an eyebrow, much like an unexpected fake punt in the fourth quarter.
Now, this statistical spectacle brings forth an amusing prospect: Could it be that a household's electricity bill has some mysterious influence over the fate of soaring field goals and dramatic end zone celebrations? Much like a sudden fake field goal attempt, our findings add a twist to the narrative of statistical associations and beckon us to ponder the whimsical juxtaposition of household dynamics and athletic prowess.
In a world where numbers and whimsy collide, our inquiry has delivered an unexpected touchdown of insight. The delightfully absurd correlation we uncovered between annual US household spending on electricity and the points scored by the Dallas Cowboys challenges conventional wisdom and may very well leave even the most seasoned statisticians bemused.
In summary, our findings suggest a compelling connection between the flicker of household lights and the flick of a football across the goal line. As we bask in the glow of this discovery, let's not forget to appreciate the statistical symphony that unfolds before us, where the unexpected harmonizes with the empirical.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our study has brought to light a shockingly robust correlation between annual US household spending on electricity and the points scored by the Dallas Cowboys – a delightful revelation that has electrified the world of statistics and sports analysis. As we set out on this whimsical endeavor, critics might have scoffed at the notion of a connection between the mundane necessity of electricity and the high-flying theatrics of professional football. However, much like an underdog team defying the odds, our findings have defied expectations and, in doing so, have breathed new life into the playful dance between household economics and sports prowess.

The results of our investigation lend credence to prior research on the eccentric nexus between household expenditures and athletic performance. As we reflect on the scholarly morsels we gleaned from "The Glory of Their Times" and "Moneyball," it becomes evident that our discovery aligns with the spirit of these literary works. While Ritter and Lewis may not have explicitly delved into the quirk of electricity bills and touchdown celebrations, their work has inspired us to seek beyond the conventional bounds of statistical analysis and to embrace the whimsy of unexpected connections.

Similarly, our findings align with the surprising insights uncovered by Smith and Doe in their study on household economics. While their research may not have directly probed the comical correlation we illuminate, the broader context of consumer behavior and expenditure patterns provides a fitting backdrop for our own discovery. Who would've thought that the flickering of a household light bulb could hold sway over the flick of a football across the goal line? In the grand theater of statistics, it seems that our findings have added an unexpected subplot to the tale of consumer behavior and athletic achievement.

Our results further support the notion put forth in "Jones's Gridiron Gazette: Musings on Sports, Statistics, and Shenanigans," where the authors playfully toyed with the idea that the ecstasy of spectating a football game might permeate consumer behavior. While our study may not have confirmed this notion outright, the tantalizing correlation we uncovered certainly alludes to the enduring influence of sports on the minutiae of household dynamics.

In concert with these prior insights, our study has illuminated a sizzling correlation between two seemingly disparate domains. The relationship between household electricity expenditure and the Cowboys' performance, as highlighted by the robust correlation coefficient and statistically significant p-value, takes its place in the annals of statistical curiosities. Just as unexpected trick plays can change the course of a football game, our unexpected findings beckon us to contemplate the whimsical interplay between residential energy consumption and the gamut of athletic feats.

In weaving together the strands of statistical significance and comical correlation, our investigation has sparked a delightful symphony of unexpected connections. The pulsating beat of this statistical waltz will undoubtedly linger in the scholarly halls of research, prompting future inquiries into the peculiarity of statistical associations. As we savor this statistical touchdown, let us not forget to revel in the delightful tango between the quotidian and the utterly preposterous – for in this nexus lies the heart of the unexpected and the empirical.


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 Annual US household spending on electricity and Points scored by the Dallas Cowboys. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation

[[ABSTRACT]]
This study delves into the peculiar phenomenon of the relationship between annual US household spending on electricity and the points scored by the Dallas Cowboys, bridging the disparate realms of household economics and gridiron glory. Leveraging data from the Bureau of Labor Statistics and Pro-Football-Reference.com, our research team scrutinized the perplexing nexus between power bills and pigskin prowess. In a surprising twist worthy of a dramatic game-winning touchdown, we unearthed a robust correlation coefficient of 0.7373286 and a statistically significant p-value of less than 0.01 for the period spanning from 2000 to 2022. Our findings suggest that there may well be an electrifying connection between the flicker of household lights and the flick of a football across the goal line -- a delightful discovery that may leave skeptics and statisticians alike scratching their heads in bemusement.

[[INTRDUCTION]]
Behold, dear readers, as we embark on an electrifying journey into the whimsical world of statistics and sports – an odyssey that unveils the seemingly incongruous link between the annual US household spending on electricity and the points scored by none other than the illustrious Dallas Cowboys. This peculiar pairing of variables presents rich fodder for investigation, conjuring a curious confluence of realms that straddle the domestic economy and the grand stage of the gridiron.
As we delve into this comical correlation, it is prudent to acknowledge the sheer audacity of such an undertaking. Who would envisage that the flicker of a light bulb in Omaha could hold any sway over the fate of a touchdown in Dallas? A skeptic may scoff at the very notion, and a stringent statistician may raise an eyebrow in incredulity. Yet, in the unfathomable depths of data lay intriguing patterns waiting to emerge, and it is with a mix of prudence and jest that we wade into this enigmatic sea of numbers.
Our noble quest is propelled by an unquenchable thirst for uncovering the hidden threads that weave through the fabric of our existence – or in the whimsical world of data analysis, the covariation that binds disparate variables. The very notion of household energy expenses influencing the performance of a storied football franchise may provoke a chuckle or two, but the pursuit of knowledge knows no bounds, not even the boundaries of conventional wisdom.
In this investigator's paradise, we have harnessed the power of quantitative analysis, let loose the p-value pirates, and marshaled the forces of regression to discern whether there exists a correlation between household electricity spending and the efficacy of field goals. The scientific method stands as our guiding lodestar, although a dash of humor and a sprinkle of mirth shall never be amiss as we navigate this unorthodox territory.
So, ready your calculators, keep an eye on that p-value, and prepare to revel in the delightful absurdity of our findings. For as we peer deep into the heart of this statistical tapestry, we may find that the hum of the microwave and the hum of the crowd are not as distant as once presumed. The game of correlations awaits, and the wisdom that we uncover from this whimsical inquiry may well light the path to future revelations – or, at the very least, provide amusement for the discerning reader.

[[RESULTS]]
Our intrepid exploration into the zany world of statistics and sports has yielded some truly electrifying results. The correlation coefficient between annual US household spending on electricity and the points scored by the Dallas Cowboys from 2000 to 2022 stood at a shocking 0.7373286, accompanied by an r-squared value of 0.5436535. To top it off, the p-value struts in with flair, boasting a significance level of less than 0.01. It seems that we have stumbled upon a statistical touchdown here!
In Figure 1, dear readers, we present our pièce de résistance – a captivating scatterplot that vividly illustrates the striking correlation between these unlikely bedfellows. The data points dance gleefully across the chart, twirling in perfect synchrony, much like a well-coordinated touchdown drive.
It's fair to say that the correlation we uncovered is truly "current." With an r-squared value of 0.5436535, more than half of the variation in the points scored by the Dallas Cowboys can be explained by the variation in household electricity spending. We might amuse ourselves with the notion that, just like power surges dictate the ebb and flow of energy in a household, the Cowboys' triumphs and setbacks are intertwined with the vicissitudes of power consumption.
The correlation coefficient of 0.7373286 indicates a strong positive relationship between the two variables. One might quip that the connection between household electricity expenditure and the Cowboys' performance is as luminous as a floodlit football stadium on a crisp autumn evening. This finding tickles the fancy and raises an eyebrow, much like an unexpected fake punt in the fourth quarter.
Now, this statistical spectacle brings forth an amusing prospect: Could it be that a household's electricity bill has some mysterious influence over the fate of soaring field goals and dramatic end zone celebrations? Much like a sudden fake field goal attempt, our findings add a twist to the narrative of statistical associations and beckon us to ponder the whimsical juxtaposition of household dynamics and athletic prowess.
In a world where numbers and whimsy collide, our inquiry has delivered an unexpected touchdown of insight. The delightfully absurd correlation we uncovered between annual US household spending on electricity and the points scored by the Dallas Cowboys challenges conventional wisdom and may very well leave even the most seasoned statisticians bemused.
In summary, our findings suggest a compelling connection between the flicker of household lights and the flick of a football across the goal line. As we bask in the glow of this discovery, let's not forget to appreciate the statistical symphony that unfolds before us, where the unexpected harmonizes with the empirical.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In this delightfully witty study, we have shone a spotlight on the captivating correlation between annual US household spending on electricity and the points scored by the Dallas Cowboys. Our findings reveal a "current" of statistical significance, with a correlation coefficient so shockingly robust that it could power a small town! As we ponder the bizarre interplay between power bills and pigskin prowess, it becomes evident that statistical analysis is not simply a numbers game, but a grand theater of the absurd where unexpected correlations lurk like well-concealed plays in a football playbook.

The r-squared value of 0.5436535 unveils a whimsical world where more than half of the variation in the Cowboys' points can be illuminated by the vicissitudes of household electricity spending. It's as if the ebb and flow of power consumption dictates the ebb and flow of touchdowns and field goals -- a comical dance of statistical significance that would make even the most stoic statistician crack a bemused smile.

Our findings challenge conventional wisdom, beckoning us to ponder the whimsical juxtaposition of household economics and athletic clout. The correlation we uncovered is not just a statistical quirk; it's a glorious revelation that adds a delightful twist to the narrative of empirical inquiry. Indeed, who would have thought that the flicker of a light bulb in Omaha could hold any sway over the fate of a touchdown in Dallas? It's a tale that engages the imagination and tickles the fancy, much like an unexpected flea-flicker play in the final minutes of a nail-biting game.

So, as we unfurl the banner of statistical revelry, let us embrace the sheer absurdity of our findings and celebrate the statistical symphony that unfolds before us. With a jovial wink to the whims of data analysis, we assert with utmost confidence that no further research is required in this comically captivating arena. With our minds ablaze and our spirits uplifted, we bid adieu to this enigmatic realm of statistical absurdity, content in the knowledge that in the realms of empirical inquiry, the unexpected harmonizes with the empirical, and the absurd holds hands with enlightenment.


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 Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Witty Science


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 Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Silly Scientific Studies


Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Electricity Expenditure vs. Cowboys' Clout: A Comical Correlation with an abstract of This study delves into the peculiar phenomenon of the relationship between annual US household spending on electricity and the points scored by the Dallas Cowboys, bridging the disparate realms of household economics and gridiron glory. Leveraging data from the Bureau of Labor Statistics and Pro-Football-Reference.com, our research team scrutinized the perplexing nexus between power bills and pigskin prowess. In a surprising twist worthy of a dramatic game-winning touchdown, we unearthed a robust correlation coefficient of 0.7373286 and a statistically significant p-value of less than 0.01 for the period spanning from 2000 to 2022. Our findings suggest that there may well be an electrifying connection between the flicker of household lights and the flick of a football across the goal line -- a delightful discovery that may leave skeptics and statisticians alike scratching their heads in bemusement.

ChatGPT:

electricity expenditure, household spending, electricity costs, Dallas Cowboys, point scoring, gridiron statistics, household economics, power bills, correlation coefficient, statistical significance, p-value, Bureau of Labor Statistics, Pro-Football-Reference.com, energy consumption, football performance, household electricity usage

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



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Data details

Annual US household spending on electricity
Detailed data title: Average annual household spend on electricity
Source: Bureau of Labor Statistics
See what else correlates with Annual US household spending on electricity

Points scored by the Dallas Cowboys
Detailed data title: Total points scored during the year by the Dallas Cowboys
Source: Pro-Football-Reference.com
See what else correlates with Points scored by the Dallas Cowboys

Correlation r = 0.7373286 (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 = 0.5436535 (Coefficient of determination)
This means 54.4% of the change in the one variable (i.e., Points scored by the Dallas Cowboys) is predictable based on the change in the other (i.e., Annual US household spending on electricity) over the 23 years from 2000 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.0E-5. 0.0000596881735352098900000000
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 0.74 in 0.006% of random cases. Said differently, if you correlated 16,754 random variables Which I absolutely did.
with the same 22 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 22 because we have two variables measured over a period of 23 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.

[ 0.47, 0.88 ] 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.
20002001200220032004200520062007200820092010201120122013201420152016201720182019202020212022
Annual US household spending on electricity (Household spend)911100998110281064115512661303135313771413142313881422148414601444142014961472151615511683
Points scored by the Dallas Cowboys (Cowboys points)294246217289293325425455362361394369376439467275421354339434395530467




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([911,1009,981,1028,1064,1155,1266,1303,1353,1377,1413,1423,1388,1422,1484,1460,1444,1420,1496,1472,1516,1551,1683,])
array_2 = np.array([294,246,217,289,293,325,425,455,362,361,394,369,376,439,467,275,421,354,339,434,395,530,467,])
array_1_name = "Annual US household spending on electricity"
array_2_name = "Points scored by the Dallas Cowboys"

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

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



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You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

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Correlation ID: 3622 · Black Variable ID: 19910 · Red Variable ID: 19686
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