Spurious correlation #1,149 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of mobile heavy equipment mechanics in Maine and the second variable is Customer satisfaction with Verizon. The chart goes from 2004 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of mobile heavy equipment mechanics in Maine and the second variable is Customer satisfaction with Verizon. The chart goes from 2004 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of mobile heavy equipment mechanics in Maine rises, so does the overall morale of the state. With their expert skills and can-do attitude, these mechanics inadvertently spread a wave of positivity across Maine, leading to a general sense of contentment and happiness. As a result, people find themselves more patient and understanding, including when it comes to their experiences with Verizon. It's as if the mechanics are not only fixing heavy equipment, but also mending any potential frustrations with phone services. Who knew that the key to improving customer satisfaction with Verizon in Maine lay in the grease-stained hands of these mobile mechanics? It's clear that their impact goes beyond just machinery - they're providing a smooth ride for telecommunications too!

^{Show image generation prompt} Model: dalle-3
Prompt: Create an image where the landscape is divided into three layers. The foreground depicts a group of technicians in heavy work attire maneuvering specialized equipment to carefully repair the network of underground tunnels. The middle ground portrays the tunnels, made visible by a slight glow revealing the carrier pigeons within. In the background, customers are seen happily conversing on their phones, with radiant smiles on their faces, while the signal strength of Verizon visibly improves. Each layer should exhibit depth and a sense of movement. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the situation described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here is a situation: The increase in the number of mobile heavy equipment mechanics in Maine caused customer satisfaction with Verizon to also increase because those mechanics were able to fix the underground network of carrier pigeon tunnels that Verizon uses to transmit signals in rural areas. With the tunnels functioning more efficiently, the signal strength improved and customers were finally able to hear each other without sounding like they were speaking whale.. Given that situation, which of the following image descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand and the tone of the explanation. Please respond with only one of the following potential descriptions: Photorealism Cutsy art illustration An image that looks like it is a scene from 'Star Wars' Charcoal Parallax art
ChatGPT: Parallax art

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated and the explanation described. Do not reference graphs or charts; describe physical objects.
User prompt: Please describe a visual scene that depicts the following situation. Keep the description simple. The increase in the number of mobile heavy equipment mechanics in Maine caused customer satisfaction with Verizon to also increase because those mechanics were able to fix the underground network of carrier pigeon tunnels that Verizon uses to transmit signals in rural areas. With the tunnels functioning more efficiently, the signal strength improved and customers were finally able to hear each other without sounding like they were speaking whale.
ChatGPT: In a rural area of Maine, we see a group of mobile heavy equipment mechanics diligently working on repairing a network of underground tunnels. The mechanics are using specialized equipment to carefully fix the tunnels, which appear to be carrying carrier pigeons. As the mechanics work, we observe the signal strength of Verizon improving, resulting in delighted customers who are conversing on their phones without any distortion, with happy expressions on their faces.

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: [ Parallax art ] and depicting this scene: In a rural area of Maine, we see a group of mobile heavy equipment mechanics diligently working on repairing a network of underground tunnels. The mechanics are using specialized equipment to carefully fix the tunnels, which appear to be carrying carrier pigeons. As the mechanics work, we observe the signal strength of Verizon improving, resulting in delighted customers who are conversing on their phones without any distortion, with happy expressions on their faces. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image where the landscape is divided into three layers. The foreground depicts a group of technicians in heavy work attire maneuvering specialized equipment to carefully repair the network of underground tunnels. The middle ground portrays the tunnels, made visible by a slight glow revealing the carrier pigeons within. In the background, customers are seen happily conversing on their phones, with radiant smiles on their faces, while the signal strength of Verizon visibly improves. Each layer should exhibit depth and a sense of movement.

*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 The number of mobile heavy equipment mechanics in Maine caused Customer satisfaction with Verizon to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon

The Journal of Applied Mechanics and Customer Satisfaction

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 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 The number of mobile heavy equipment mechanics in Maine and Customer satisfaction with Verizon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and American Customer Satisfaction Index . You used data from 2004 to 2021

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon"

[[ABSTRACT]]
In this research paper, we examine the unexpected link between the number of mobile heavy equipment mechanics in the state of Maine and customer satisfaction with Verizon. Through the ingenious use of Bureau of Labor Statistics data and the American Customer Satisfaction Index, we sought to unravel this enigmatic correlation. Our findings reveal a surprisingly robust correlation coefficient of 0.9426328, with statistical significance at p < 0.01 for the years spanning 2004 to 2021. As we delve into the perplexing world of heavy equipment mechanics and their influence on telecommunications consumer sentiment, we unearth valuable insights and perhaps a few tire tracks along the way. Join us on this scholarly expedition as we explore the unanticipated synergy between Maine's mechanical workforce and the wireless wonders of customer satisfaction with Verizon.

[[INTRODUCTION]]
Introduction
The relationship between customer satisfaction and various economic, social, and demographic factors has been an enduring subject of scholarly inquiry. As researchers, we are continually fascinated by the unexpected connections and convoluted influences that shape consumer sentiment. In this paper, we delve into the captivating realm of heavy equipment mechanics and their intricate interplay with customer satisfaction with Verizon, the renowned telecommunications giant. Our investigation stems from an unwavering curiosity and a perhaps slightly eccentric sense of scholarly adventure as we navigate the terrain of Maine's labor force and the ethereal waves of wireless communication.
While the conventional wisdom might suggest that customer satisfaction with a telecommunications provider is primarily driven by network coverage, pricing, and customer service, our findings unveil a quirkier quagmire of correlations. We have heeded the call to uncover the entwined fate of mobile heavy equipment mechanics and the contentment of Verizon customers, defying the mundane and embracing the peculiar in our pursuit of insight.
In the hallowed halls of academia, where statistical significance reigns supreme, we probed the Bureau of Labor Statistics data with diligence, peering through the data's hexadecimal haze to distill the essence of Maine's mobile heavy equipment mechanics. With statistical rigor as our trusty guide, we embarked on a journey to elucidate the enigmatic and, dare we say, slightly unorthodox connection between wrenches and wireless signals.
Throughout this scholarly odyssey, we encountered peculiar patterns and unexpected meanderings amid the numerical underbrush. The correlation coefficient of 0.9426328 that emerged from our mathematical musings serves as a testament to the robustness of the connection we sought to unravel. And as any astute scholar knows, a correlation of such magnitude begs deeper exploration, beckoning intrepid researchers to wander beyond the beaten path of conventional wisdom.
As we embark on this outlandish escapade, armed with spreadsheets and ponderous tomes of statistical theory, let us not forget to pause and savor the absurdity of our scholarly pursuits. In the intersection of heavy machinery and wireless wizardry, who knows what surprises await? The call of empirical evidence and the allure of intellectual discovery beckon us forward, as we navigate the peculiar crossover between Maine's mechanical virtuosos and the telephonic tapestry of customer satisfaction with Verizon.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Sample Selection
The selection of our sample of mobile heavy equipment mechanics in the state of Maine was a task that required meticulous attention to detail, a dash of resourcefulness, and perhaps a modicum of fascination with all things heavy and mechanized. We initially scoured the Bureau of Labor Statistics database, sifting through the digital haystack to pinpoint the elusive needles of vocational data. Our criteria for inclusion were as stringent as they were whimsical – we sought only those intrepid souls whose mechanical prowess extended to the mobile realm and whose professional domicile was within the picturesque borders of Maine.

Data Collection
With our sample thus established, we embarked on a grand journey through the annals of time, or more precisely, the data archives spanning the years from 2004 to 2021. Our data collection efforts involved a symphony of mouse clicks, keyboard clatter, and the occasional exasperated sigh as we navigated the labyrinthine corridors of the internet. While the Bureau of Labor Statistics served as our primary wellspring of occupational data, we also ventured into the cradle of numerical insight known as the American Customer Satisfaction Index, where the ebb and flow of consumer sentiment awaited our scholarly scrutiny.

Variables of Interest
Ah, the dance of variables – those captivating components of our statistical waltz that pirouette and promenade through the halls of regression analysis. For the discerning eye of the researcher, the choice of variables is an artful exercise in balance and precision. In our exploration of the interplay between Maine's mobile heavy equipment mechanics and the tapestry of customer satisfaction with Verizon, we set our sights on an array of variables, from employment numbers to consumer satisfaction scores. The former spoke to the heart of Maine's labor force, while the latter whispered secrets about the enigmatic dance between telecommunications prowess and customer contentment.

Data Analysis
Armed with spreadsheets, statistical software, and a generous dose of intrigued bewilderment, we embarked on the riveting endeavor of data analysis. Our statistical arsenal included correlation coefficients, regression models, and the occasional scatter plot that invited us to connect the dots – both figuratively and literally. We meticulously waded through the numerical quagmire, donning our statistical hip waders as we traversed the treacherous terrain of data visualization and model estimation. The correlation coefficient of 0.9426328 emerged from this mathematical menagerie, standing tall as a numerical testament to the intertwined fate of wrenches and wireless signals.

Ethical Considerations
In our scholarly pursuit, we upheld the sacred tenets of ethical research conduct with unwavering resolve. The data we utilized were sourced from publicly available repositories, with due attention to intellectual property rights and bibliographic etiquette. Our analyses were conducted with a judicious regard for statistical integrity, for in the hallowed halls of academia, the torch of ethical research illuminates the path toward knowledge and understanding.

Limitations
As with all scholarly undertakings, our research was not without its limitations. The reliance on aggregated data constrained our ability to delve into individual nuances and granular intricacies that may have shaped the observed correlations. Furthermore, the temporal span of our analysis, spanning from 2004 to 2021, may not encapsulate the entirety of the dynamic interplay between Maine's mobile heavy equipment mechanics and the domain of wireless consumer satisfaction. These limitations, while sobering, serve as a call to future researchers to venture further into the uncharted wilderness of empirical inquiry.

In conclusion, our methodological approach ventured beyond the conventional and embraced the idiosyncratic, as we sought to untangle the enigmatic connection between Maine's mechanical aficionados and the telephonic tapestry of customer satisfaction with Verizon. The synergy of statistical rigor and scholarly eccentricity guided our scholarly odyssey, weaving a narrative of discovery amid the numerical ether.

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 The number of mobile heavy equipment mechanics in Maine and Customer satisfaction with Verizon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and American Customer Satisfaction Index .

For the time period 2004 to 2021, you found a correlation 0.9426328, r-squared of 0.8885565, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
"The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon"

[[ABSTRACT]]
In this research paper, we examine the unexpected link between the number of mobile heavy equipment mechanics in the state of Maine and customer satisfaction with Verizon. Through the ingenious use of Bureau of Labor Statistics data and the American Customer Satisfaction Index, we sought to unravel this enigmatic correlation. Our findings reveal a surprisingly robust correlation coefficient of 0.9426328, with statistical significance at p < 0.01 for the years spanning 2004 to 2021. As we delve into the perplexing world of heavy equipment mechanics and their influence on telecommunications consumer sentiment, we unearth valuable insights and perhaps a few tire tracks along the way. Join us on this scholarly expedition as we explore the unanticipated synergy between Maine's mechanical workforce and the wireless wonders of customer satisfaction with Verizon.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Introduction
The relationship between customer satisfaction and various economic, social, and demographic factors has been an enduring subject of scholarly inquiry. As researchers, we are continually fascinated by the unexpected connections and convoluted influences that shape consumer sentiment. In this paper, we delve into the captivating realm of heavy equipment mechanics and their intricate interplay with customer satisfaction with Verizon, the renowned telecommunications giant. Our investigation stems from an unwavering curiosity and a perhaps slightly eccentric sense of scholarly adventure as we navigate the terrain of Maine's labor force and the ethereal waves of wireless communication.
While the conventional wisdom might suggest that customer satisfaction with a telecommunications provider is primarily driven by network coverage, pricing, and customer service, our findings unveil a quirkier quagmire of correlations. We have heeded the call to uncover the entwined fate of mobile heavy equipment mechanics and the contentment of Verizon customers, defying the mundane and embracing the peculiar in our pursuit of insight.
In the hallowed halls of academia, where statistical significance reigns supreme, we probed the Bureau of Labor Statistics data with diligence, peering through the data's hexadecimal haze to distill the essence of Maine's mobile heavy equipment mechanics. With statistical rigor as our trusty guide, we embarked on a journey to elucidate the enigmatic and, dare we say, slightly unorthodox connection between wrenches and wireless signals.
Throughout this scholarly odyssey, we encountered peculiar patterns and unexpected meanderings amid the numerical underbrush. The correlation coefficient of 0.9426328 that emerged from our mathematical musings serves as a testament to the robustness of the connection we sought to unravel. And as any astute scholar knows, a correlation of such magnitude begs deeper exploration, beckoning intrepid researchers to wander beyond the beaten path of conventional wisdom.
As we embark on this outlandish escapade, armed with spreadsheets and ponderous tomes of statistical theory, let us not forget to pause and savor the absurdity of our scholarly pursuits. In the intersection of heavy machinery and wireless wizardry, who knows what surprises await? The call of empirical evidence and the allure of intellectual discovery beckon us forward, as we navigate the peculiar crossover between Maine's mechanical virtuosos and the telephonic tapestry of customer satisfaction with Verizon.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The statistical analysis of the data yielded remarkable insights into the relationship between the number of mobile heavy equipment mechanics in Maine and customer satisfaction with Verizon. The correlation coefficient of 0.9426328 indicated a strong positive association between these seemingly disparate variables. This coefficient was complemented by an r-squared value of 0.8885565, signifying that approximately 88.86% of the variation in customer satisfaction with Verizon could be explained by the number of mobile heavy equipment mechanics in Maine. The p-value of less than 0.01 further underscored the robustness and statistical significance of our findings.

Figure 1 presents a visual representation of the observed correlation, displaying a scatterplot that elucidates the compelling relationship between the two variables. The plot serves as a testament to the undeniable synergy between Maine's mechanical workforce and the consumer sentiments swirling in the telecommunications domain. It also serves as a subtle reminder that even in the realm of scholarly pursuits, numbers and data can be surprisingly expressive.

The strength of the correlation prompts contemplation of the intricate mechanisms at play in shaping customer sentiments toward Verizon. While conventional factors such as network quality and pricing undoubtedly exert substantial influence, our findings open the door to an unexpected dimension of influence emanating from the vibrant world of heavy equipment mechanics. In the vein of literary analysis, one could consider the heavy equipment mechanics as characters in a grand narrative of customer satisfaction, adding their unique flavor to the overarching plot of telecommunication consumerism.

Our results not only attest to the statistical significance of the observed relationship but also beckon further exploration into the nuanced interplay of factors shaping customer satisfaction in the telecommunications landscape. They also suggest that a deeper understanding of consumer sentiment in this context may require a broader appreciation of the multifaceted influences that underpin it. After all, in this scholarly saga, the cast of characters extends beyond mere human consumers and includes the unsung heroes of the heavy equipment repair world.

In conclusion, the unanticipated alliance between the number of mobile heavy equipment mechanics in Maine and customer satisfaction with Verizon uncovers a curious nexus of influence. These findings, unexpected as they may be, invite scholarly contemplation and raise the intriguing possibility of hitherto unexplored dynamics shaping customer satisfaction within the telecommunications sphere. The intersection of Maine's mechanical artisans and the capricious currents of consumer sentiment serves as a fertile ground for further scholarly inquiry, offering a tantalizing glimpse into the convergence of the tangible and the intangible in the labyrinthine world of customer satisfaction.

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 The number of mobile heavy equipment mechanics in Maine and Customer satisfaction with Verizon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
"The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon"

[[ABSTRACT]]
In this research paper, we examine the unexpected link between the number of mobile heavy equipment mechanics in the state of Maine and customer satisfaction with Verizon. Through the ingenious use of Bureau of Labor Statistics data and the American Customer Satisfaction Index, we sought to unravel this enigmatic correlation. Our findings reveal a surprisingly robust correlation coefficient of 0.9426328, with statistical significance at p < 0.01 for the years spanning 2004 to 2021. As we delve into the perplexing world of heavy equipment mechanics and their influence on telecommunications consumer sentiment, we unearth valuable insights and perhaps a few tire tracks along the way. Join us on this scholarly expedition as we explore the unanticipated synergy between Maine's mechanical workforce and the wireless wonders of customer satisfaction with Verizon.

[[LITERATURE REVIEW]]
The curious correlation between the number of mobile heavy equipment mechanics in Maine and customer satisfaction with Verizon has spurred a diverse array of scholarly investigations, ranging from the ostensibly serious to the surprisingly silly. In "Smith et al.," the authors raise the curtain on this enigmatic linkage, laying a foundation for the exploration that follows. Building upon this groundwork, "Doe" delves into the socioeconomic dynamics of mechanic-telecom entanglements, providing insights that are as intriguing as they are unexpected.
Moving beyond the traditional academic realm, we encounter "Heavy Machinery Monthly," a gripping compendium of gearhead wisdom and axle-spinning revelations. Its anecdotal accounts of technicians' encounters with cellular towers add a refreshingly personal touch to the investigation. However, the scholarly discourse would be incomplete without a nod to the fictional universe. Enter "The Mystical Mechanics of Maine," a fanciful novel that weaves a tale of divinely ordained repairmen and fateful encounters with wireless devices. Though its authenticity may be questionable, its portrayal of the intertwining threads of automotive ingenuity and telecommunications intrigue cannot be disregarded.
As we embarked on our scholarly journey, we found ourselves venturing into uncharted territory, resorting to unconventional sources for insights that traditional academia might deem frivolous. Perusing the narratives of TV shows such as "Maine Mechanics: Unearthed" and "V for Verizon," we gathered anecdotal evidence and subtle nuances that played a role in shaping our understanding of this improbable but undeniable nexus.
While the unconventional nature of these sources may raise eyebrows in certain scholarly circles, we embraced them as indispensable companions on our quest for knowledge. For in the perplexing pursuit of understanding Maine's mechanical maestros and their influence on telecommunications consumer sentiment, one must be open to both the empirical and the whimsical, the substantiated and the speculative.

[[RESULTS]]
The statistical analysis of the data yielded remarkable insights into the relationship between the number of mobile heavy equipment mechanics in Maine and customer satisfaction with Verizon. The correlation coefficient of 0.9426328 indicated a strong positive association between these seemingly disparate variables. This coefficient was complemented by an r-squared value of 0.8885565, signifying that approximately 88.86% of the variation in customer satisfaction with Verizon could be explained by the number of mobile heavy equipment mechanics in Maine. The p-value of less than 0.01 further underscored the robustness and statistical significance of our findings.
Figure 1 presents a visual representation of the observed correlation, displaying a scatterplot that elucidates the compelling relationship between the two variables. The plot serves as a testament to the undeniable synergy between Maine's mechanical workforce and the consumer sentiments swirling in the telecommunications domain. It also serves as a subtle reminder that even in the realm of scholarly pursuits, numbers and data can be surprisingly expressive.
The strength of the correlation prompts contemplation of the intricate mechanisms at play in shaping customer sentiments toward Verizon. While conventional factors such as network quality and pricing undoubtedly exert substantial influence, our findings open the door to an unexpected dimension of influence emanating from the vibrant world of heavy equipment mechanics. In the vein of literary analysis, one could consider the heavy equipment mechanics as characters in a grand narrative of customer satisfaction, adding their unique flavor to the overarching plot of telecommunication consumerism.
Our results not only attest to the statistical significance of the observed relationship but also beckon further exploration into the nuanced interplay of factors shaping customer satisfaction in the telecommunications landscape. They also suggest that a deeper understanding of consumer sentiment in this context may require a broader appreciation of the multifaceted influences that underpin it. After all, in this scholarly saga, the cast of characters extends beyond mere human consumers and includes the unsung heroes of the heavy equipment repair world.
In conclusion, the unanticipated alliance between the number of mobile heavy equipment mechanics in Maine and customer satisfaction with Verizon uncovers a curious nexus of influence. These findings, unexpected as they may be, invite scholarly contemplation and raise the intriguing possibility of hitherto unexplored dynamics shaping customer satisfaction within the telecommunications sphere. The intersection of Maine's mechanical artisans and the capricious currents of consumer sentiment serves as a fertile ground for further scholarly inquiry, offering a tantalizing glimpse into the convergence of the tangible and the intangible in the labyrinthine world of customer satisfaction.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

As we reflect on the captivating convergence of Maine's mobile heavy equipment mechanics and the realm of Verizon's customer satisfaction, our findings solidify the previously unearthed connections and introduce a new layer of intrigue to this unlikely relationship.

Our research not only corroborates the prior propositions put forth by Smith et al. and Doe but also sheds empirical light on the intricate interplay between these ostensibly incongruent variables. The statistical robustness of the correlation coefficient, with a staggering value of 0.9426328, lends quantitative weight to the scholarly musings that preceded our investigation. It seems that the socio-economic dynamics portrayed by Doe and the anecdotal accounts of tower technicians in Heavy Machinery Monthly were not mere whimsical ponderings but rather precursors to the empirical revelations we have now unveiled.

The strength of the correlation, as indicated by the r-squared value of 0.8885565, speaks volumes about the seemingly unassuming influence of Maine's mechanical wizards on the ethereal fabric of consumer sentiment. While these individuals may not wear capes or possess cosmic powers, our findings position them as unsung heroes in the grand narrative of Verizon's customer satisfaction. Their tools and technical prowess, it seems, are not merely instruments for repairing heavy equipment but also inadvertently touchstones in shaping the attitudes and perceptions of Verizon's patrons.

In light of our results, it is worth reconsidering the multifaceted influences that constitute the theatrical performance of customer satisfaction. Like protagonists in a prodigious play, the heavy equipment mechanics take their place alongside the concealed actors of network quality and pricing, adding an unexpected twist to the script of consumer sentiment. As their impact on the narrative unfolds, one cannot help but appreciate the subtle irony dripping from this scholarly drama – that those who are tasked with mending machines have unknowingly constructed an invisible, yet quantifiably significant, bridge to the world of telecommunications consumerism.

Figure 1, our visual testament to this intriguing nexus, not only encapsulates the numerical underpinnings of our discoveries but also serves as a reminder that in the realm of scholarly pursuits, even scatterplots can be captivating voyages into the uncharted territories of human phenomena. It is a sobering observation that our scholarly pursuits, driven as they may be by logic and rationality, are occasionally nudged into exhilarating esoterica by the revelatory disguises of data.

In conclusion, our findings invite scholars to embark upon further expeditions into the unorthodox alliances and whimsical connections that underpin the intricate tapestry of consumer sentiment. These unconventional entanglements have elicited a chuckle of mirth in our scholarly repertoire, raising our appreciation for the whimsical and unexpected influences that mold the landscape of customer satisfaction. As we peer through the lens of scholarly inquiry, the unanticipated convergence of Maine's mechanical artisans and Verizon's consumer satisfaction emerges as a beacon guiding us toward the unexplored reaches of interdisciplinary discourse and perhaps the occasional lighthearted jest.

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 The number of mobile heavy equipment mechanics in Maine and Customer satisfaction with Verizon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon"

[[ABSTRACT]]
In this research paper, we examine the unexpected link between the number of mobile heavy equipment mechanics in the state of Maine and customer satisfaction with Verizon. Through the ingenious use of Bureau of Labor Statistics data and the American Customer Satisfaction Index, we sought to unravel this enigmatic correlation. Our findings reveal a surprisingly robust correlation coefficient of 0.9426328, with statistical significance at p < 0.01 for the years spanning 2004 to 2021. As we delve into the perplexing world of heavy equipment mechanics and their influence on telecommunications consumer sentiment, we unearth valuable insights and perhaps a few tire tracks along the way. Join us on this scholarly expedition as we explore the unanticipated synergy between Maine's mechanical workforce and the wireless wonders of customer satisfaction with Verizon.

[[INTRDUCTION]]
Introduction
The relationship between customer satisfaction and various economic, social, and demographic factors has been an enduring subject of scholarly inquiry. As researchers, we are continually fascinated by the unexpected connections and convoluted influences that shape consumer sentiment. In this paper, we delve into the captivating realm of heavy equipment mechanics and their intricate interplay with customer satisfaction with Verizon, the renowned telecommunications giant. Our investigation stems from an unwavering curiosity and a perhaps slightly eccentric sense of scholarly adventure as we navigate the terrain of Maine's labor force and the ethereal waves of wireless communication.
While the conventional wisdom might suggest that customer satisfaction with a telecommunications provider is primarily driven by network coverage, pricing, and customer service, our findings unveil a quirkier quagmire of correlations. We have heeded the call to uncover the entwined fate of mobile heavy equipment mechanics and the contentment of Verizon customers, defying the mundane and embracing the peculiar in our pursuit of insight.
In the hallowed halls of academia, where statistical significance reigns supreme, we probed the Bureau of Labor Statistics data with diligence, peering through the data's hexadecimal haze to distill the essence of Maine's mobile heavy equipment mechanics. With statistical rigor as our trusty guide, we embarked on a journey to elucidate the enigmatic and, dare we say, slightly unorthodox connection between wrenches and wireless signals.
Throughout this scholarly odyssey, we encountered peculiar patterns and unexpected meanderings amid the numerical underbrush. The correlation coefficient of 0.9426328 that emerged from our mathematical musings serves as a testament to the robustness of the connection we sought to unravel. And as any astute scholar knows, a correlation of such magnitude begs deeper exploration, beckoning intrepid researchers to wander beyond the beaten path of conventional wisdom.
As we embark on this outlandish escapade, armed with spreadsheets and ponderous tomes of statistical theory, let us not forget to pause and savor the absurdity of our scholarly pursuits. In the intersection of heavy machinery and wireless wizardry, who knows what surprises await? The call of empirical evidence and the allure of intellectual discovery beckon us forward, as we navigate the peculiar crossover between Maine's mechanical virtuosos and the telephonic tapestry of customer satisfaction with Verizon.

[[RESULTS]]
The statistical analysis of the data yielded remarkable insights into the relationship between the number of mobile heavy equipment mechanics in Maine and customer satisfaction with Verizon. The correlation coefficient of 0.9426328 indicated a strong positive association between these seemingly disparate variables. This coefficient was complemented by an r-squared value of 0.8885565, signifying that approximately 88.86% of the variation in customer satisfaction with Verizon could be explained by the number of mobile heavy equipment mechanics in Maine. The p-value of less than 0.01 further underscored the robustness and statistical significance of our findings.
Figure 1 presents a visual representation of the observed correlation, displaying a scatterplot that elucidates the compelling relationship between the two variables. The plot serves as a testament to the undeniable synergy between Maine's mechanical workforce and the consumer sentiments swirling in the telecommunications domain. It also serves as a subtle reminder that even in the realm of scholarly pursuits, numbers and data can be surprisingly expressive.
The strength of the correlation prompts contemplation of the intricate mechanisms at play in shaping customer sentiments toward Verizon. While conventional factors such as network quality and pricing undoubtedly exert substantial influence, our findings open the door to an unexpected dimension of influence emanating from the vibrant world of heavy equipment mechanics. In the vein of literary analysis, one could consider the heavy equipment mechanics as characters in a grand narrative of customer satisfaction, adding their unique flavor to the overarching plot of telecommunication consumerism.
Our results not only attest to the statistical significance of the observed relationship but also beckon further exploration into the nuanced interplay of factors shaping customer satisfaction in the telecommunications landscape. They also suggest that a deeper understanding of consumer sentiment in this context may require a broader appreciation of the multifaceted influences that underpin it. After all, in this scholarly saga, the cast of characters extends beyond mere human consumers and includes the unsung heroes of the heavy equipment repair world.
In conclusion, the unanticipated alliance between the number of mobile heavy equipment mechanics in Maine and customer satisfaction with Verizon uncovers a curious nexus of influence. These findings, unexpected as they may be, invite scholarly contemplation and raise the intriguing possibility of hitherto unexplored dynamics shaping customer satisfaction within the telecommunications sphere. The intersection of Maine's mechanical artisans and the capricious currents of consumer sentiment serves as a fertile ground for further scholarly inquiry, offering a tantalizing glimpse into the convergence of the tangible and the intangible in the labyrinthine world of customer satisfaction.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the illustrious tradition of academic inquiry, our odyssey into the realm of Maine's mobile heavy equipment mechanics and their curious kinship with customer satisfaction with Verizon has yielded astonishing fruit. The conspicuous correlation coefficient of 0.9426328 beckons us to ponder the whimsical dance of statistical significance and the caprices of correlation. Despite the temptation to veer into metaphorical musings about wrenches and wireless waves intertwining in a scholarly tango, we shall resist and maintain our academic composure.

The lingering question of causation and the intricacies of this unexpected relationship tantalize the scholarly palate and offer a banquet of perplexing possibilities. Despite our best efforts to comprehend the enigmatic symphony of heavy equipment mechanics and consumer sentiment, we are left with an intellectual appetite whetted for further exploration.

In the grand theater of customer satisfaction, where conventional factors take center stage, the unexpected arrival of Maine's mechanical virtuosos throws a delightful curveball into the scholarly narrative. As we bid adieu to this particular line of inquiry, we assert with unwavering confidence that no further research is needed in this peculiar and slightly absurd arena of academic exploration. The curtain falls, and the spotlight now shifts to the next scholarly enigma awaiting its turn in the hallowed halls of empirical inquiry.

Or perhaps, to paraphrase Shakespeare, "though this be methodological madness, there is method in it."

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Applied Mechanics and Customer Satisfaction

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Applied Behavioral Engineering

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon with an abstract of In this research paper, we examine the unexpected link between the number of mobile heavy equipment mechanics in the state of Maine and customer satisfaction with Verizon. Through the ingenious use of Bureau of Labor Statistics data and the American Customer Satisfaction Index, we sought to unravel this enigmatic correlation. Our findings reveal a surprisingly robust correlation coefficient of 0.9426328, with statistical significance at p < 0.01 for the years spanning 2004 to 2021. As we delve into the perplexing world of heavy equipment mechanics and their influence on telecommunications consumer sentiment, we unearth valuable insights and perhaps a few tire tracks along the way. Join us on this scholarly expedition as we explore the unanticipated synergy between Maine's mechanical workforce and the wireless wonders of customer satisfaction with Verizon.

ChatGPT:

Maine, heavy equipment mechanics, customer satisfaction, Verizon, Bureau of Labor Statistics, American Customer Satisfaction Index, correlation coefficient, statistical significance, telecommunications consumer sentiment, mechanical workforce, wireless customer satisfaction

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

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Data details

The number of mobile heavy equipment mechanics in Maine
Detailed data title: BLS estimate of mobile heavy equipment mechanics, except engines in Maine
Source: Bureau of Larbor Statistics
See what else correlates with The number of mobile heavy equipment mechanics in Maine

Customer satisfaction with Verizon
Source: American Customer Satisfaction Index
See what else correlates with Customer satisfaction with Verizon

Correlation r = 0.9426328 (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 = 0.8885565 (Coefficient of determination)
This means 88.9% of the change in the one variable (i.e., Customer satisfaction with Verizon) is predictable based on the change in the other (i.e., The number of mobile heavy equipment mechanics in Maine) over the 18 years from 2004 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.9E-9.^Show 0.0000000049227948084750415000
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 0.94 in 4.9E-7% of random cases. Said differently, if you correlated 203,136,641 random variables^Note You don't actually need 203 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 17 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 17 because we have two variables measured over a period of 18 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.

[ 0.85, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
The number of mobile heavy equipment mechanics in Maine (Laborers)	760	610	710	880	960	1000	930	900	850	980	1040	870	780	1040	1110	1100	1030	1110
Customer satisfaction with Verizon (Consumer Satisfaction)	68	67	69	71	72	74	73	72	70	73	75	71	71	74	74	74	74	74

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([760,610,710,880,960,1000,930,900,850,980,1040,870,780,1040,1110,1100,1030,1110,])
array_2 = np.array([68,67,69,71,72,74,73,72,70,73,75,71,71,74,74,74,74,74,])
array_1_name = "The number of mobile heavy equipment mechanics in Maine"
array_2_name = "Customer satisfaction with Verizon"

# 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 The number of mobile heavy equipment mechanics in Maine
Line chart for only Customer satisfaction with Verizon
The spurious research paper: The Maine Connection: Heavy Equipment Mechanics and How They Mechanically Impact Customer Satisfaction with Verizon

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Correlation ID: 1149 · Black Variable ID: 9018 · Red Variable ID: 19882


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