Spurious correlation #1,144 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is US household spending on mortgage interest and charges and the second variable is Asthma attacks in American children. The chart goes from 2000 to 2019, 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 US household spending on mortgage interest and charges and the second variable is Asthma attacks in American children. The chart goes from 2000 to 2019, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As US household spending on mortgage interest and charges decreased, families were less stressed about their finances. This led to a more relaxed atmosphere at home. With lower stress levels, both parents and children were able to breathe easier, reducing the risk of asthma attacks. It turns out, the key to clear airways might just be financial relief!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image in the style of photorealism depicting a family scene. In the foreground, show a smiling couple holding a large bunch of fresh, vibrant organic kale. Nearby, depict children playing happily, surrounded by clean, fresh air. In the background, include a smaller pile of money, symbolizing decreased spending on mortgage interest and charges. The focus should be on the family's improved lifestyle and well-being. 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: A decrease in US household spending on mortgage interest and charges caused a decrease in asthma attacks in American children because the reduced financial stress led to parents being able to afford more organic, gluten-free, non-GMO, free-range, air-purified, artisanal kale for their kids, thus boosting their respiratory health.. 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: Symbolism A photo-realistic image Parallax art An image that looks like it is a scene from a Pixar movie Photorealism
ChatGPT: Photorealism

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. A decrease in US household spending on mortgage interest and charges caused a decrease in asthma attacks in American children because the reduced financial stress led to parents being able to afford more organic, gluten-free, non-GMO, free-range, air-purified, artisanal kale for their kids, thus boosting their respiratory health.
ChatGPT: In the first scene, there is a smaller pile of money representing decreased spending on mortgage interest and charges in a family's home. The parents are shown smiling and holding a big bunch of fresh, vibrant organic kale, symbolizing the increased purchase of healthier food options. Nearby, children are playing happily and breathing in the clean air, symbolizing the reduced asthma attacks due to improved respiratory health.

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 the first scene, there is a smaller pile of money representing decreased spending on mortgage interest and charges in a family's home. The parents are shown smiling and holding a big bunch of fresh, vibrant organic kale, symbolizing the increased purchase of healthier food options. Nearby, children are playing happily and breathing in the clean air, symbolizing the reduced asthma attacks due to improved respiratory health. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image in the style of photorealism depicting a family scene. In the foreground, show a smiling couple holding a large bunch of fresh, vibrant organic kale. Nearby, depict children playing happily, surrounded by clean, fresh air. In the background, include a smaller pile of money, symbolizing decreased spending on mortgage interest and charges. The focus should be on the family's improved lifestyle and well-being.

*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 a decrease in US household spending on mortgage interest and charges caused Asthma attacks in American children to decrease.

AI academic paper

(Because p < 0.01)

Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children

Journal of Respiratory Economics

Jan 2024

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

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics 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 US household spending on mortgage interest and charges and Asthma attacks in American children. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children

[[ABSTRACT]]
This research examines the perplexing interplay between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. We delved into the data from the Bureau of Labor Statistics and National Center for Health Statistics to address this wheezy topic. The correlation coefficient of 0.7934694 and p < 0.01 from 2000 to 2019 revealed a strong statistical relationship. Wheeze-economics, indeed!

Our findings suggest a significant positive association between household spending on mortgage interest and charges and the prevalence of asthma attacks in American children. It seems that as household spending on mortgages increases, so does the likelihood of asthma attacks in the younger population. This study illuminates the airway-clear connection between housing expenses and childhood respiratory health. Perhaps we've uncovered the wheeze behind mortgage costs.

This research provides valuable insights into the economic implications of household spending on respiratory health outcomes for children. It calls attention to another dimension of the housing market's impact on public health, showing that there may be more than meets the eye in the intersection of housing economics and children's health. In conclusion, we breathe easier with the knowledge that these findings may inspire further research and policy actions. After all, a good dad joke is like a breath of fresh air!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
A wise man once said, "Home is where the heart is, but it's also where the wheezing begins." In this study, we embark on an exploration of the curious relationship between US household spending on mortgage interest and charges and the frequency of asthma attacks in American children. As we dive into the data, we'll uncover statistical associations that leave us wheezing for more. Ah, the puns almost write themselves here!
Our research is not just an exercise in financial and medical box-ticking—it's a deep inhalation of the airway-clear connection between housing expenses and childhood respiratory health. We set out to investigate whether there is a wheezy economic downside to the American dream of homeownership. After all, where there's wheeze, there's a way to uncover the surprising connection between mortgage costs and children's health.
The study utilizes data from the Bureau of Labor Statistics and the National Center for Health Statistics, painting a picture that requires no asthma inhaler to grasp. Our investigation extends from 2000 to 2019, offering an expansive view of the relationship between household spending on mortgages and the prevalence of asthma attacks in the younger demographic. So, let's take a deep breath and dive into the data—just be sure to exhale after the joke lands.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The association between US household spending on mortgage interest and charges and the frequency of asthma attacks in American children has been the subject of several comprehensive investigations. Smith et al. (2015) explored the economic implications of housing expenses on children's respiratory health, finding a noteworthy positive correlation between mortgage costs and asthma prevalence. Similarly, Doe and Jones (2018) delved into the National Center for Health Statistics data, revealing a significant link between household spending on mortgages and the occurrence of asthma attacks in the younger population.

While these studies provide important insights into the connection between housing economics and children's health, they fail to unmask the underlying comedic potential of this research question. For a wheeze-free exploration of the economic and medical intersection, it is important to consider the broader body of literature, including non-fiction works such as "The Economics of Respiratory Health in American Children" by Coughlin and "Mortgages and Medical Mysteries" by Wheezelton.

In a parallel vein, fictional sources like "The Wheezy Mortgage Mystery" by Asthma Adams and "The House that Wheezed" by Respira Ryder offer an imaginative exploration of the perplexing interplay between housing expenses and childhood respiratory ailments.

Furthermore, social media posts have recently added a breath of fresh air to this discussion, with one user aptly summarizing the situation as, "Mortgage payments may take our breath away, but they shouldn't take our children's breath, too! #WheezeEconomics #AsthmaAwareness."

As the literature indicates, the connection between US household spending on mortgage interest and charges and asthma attacks in American children is not only a subject of substantive concern but also a rich source of whimsical wordplay. In light of these findings, it is essential to consider the potential for a wheezingly delightful approach to future research in this area. After all, a good dad joke is like a breath of fresh air!

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics 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 US household spending on mortgage interest and charges and Asthma attacks in American children. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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 National Center for Health Statistics . You used data from 2000 to 2019

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]]
Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children

[[ABSTRACT]]
This research examines the perplexing interplay between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. We delved into the data from the Bureau of Labor Statistics and National Center for Health Statistics to address this wheezy topic. The correlation coefficient of 0.7934694 and p < 0.01 from 2000 to 2019 revealed a strong statistical relationship. Wheeze-economics, indeed!

Our findings suggest a significant positive association between household spending on mortgage interest and charges and the prevalence of asthma attacks in American children. It seems that as household spending on mortgages increases, so does the likelihood of asthma attacks in the younger population. This study illuminates the airway-clear connection between housing expenses and childhood respiratory health. Perhaps we've uncovered the wheeze behind mortgage costs.

This research provides valuable insights into the economic implications of household spending on respiratory health outcomes for children. It calls attention to another dimension of the housing market's impact on public health, showing that there may be more than meets the eye in the intersection of housing economics and children's health. In conclusion, we breathe easier with the knowledge that these findings may inspire further research and policy actions. After all, a good dad joke is like a breath of fresh air!

[[INTRODUCTION]]
A wise man once said, "Home is where the heart is, but it's also where the wheezing begins." In this study, we embark on an exploration of the curious relationship between US household spending on mortgage interest and charges and the frequency of asthma attacks in American children. As we dive into the data, we'll uncover statistical associations that leave us wheezing for more. Ah, the puns almost write themselves here!
Our research is not just an exercise in financial and medical box-ticking—it's a deep inhalation of the airway-clear connection between housing expenses and childhood respiratory health. We set out to investigate whether there is a wheezy economic downside to the American dream of homeownership. After all, where there's wheeze, there's a way to uncover the surprising connection between mortgage costs and children's health.
The study utilizes data from the Bureau of Labor Statistics and the National Center for Health Statistics, painting a picture that requires no asthma inhaler to grasp. Our investigation extends from 2000 to 2019, offering an expansive view of the relationship between household spending on mortgages and the prevalence of asthma attacks in the younger demographic. So, let's take a deep breath and dive into the data—just be sure to exhale after the joke lands.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this research resembles a well-orchestrated symphony, with each instrument (or in this case, data collection method) playing a crucial role in the composition of our findings. To capture the economic and health dynamics under scrutiny, we engaged in a harmonious blend of quantitative analysis and statistical inference.

Data pertaining to US household spending on mortgage interest and charges was meticulously extracted from the Bureau of Labor Statistics' Consumer Expenditure Survey. We carefully combed through years of expenditure data, ensuring that no outliers or erroneous entries sneaked past our meticulous gaze. It was a bit like searching for a needle in a haystack, only the needle was a statistically significant correlation and the haystack was an extensive database of household financial records. Speaking of correlations, we were ready to pounce on any statistical relationships like a cat on a laser pointer – but with much less agility and a lot more Excel spreadsheets.

As for the variable of asthma attacks in American children, the National Center for Health Statistics' National Health Interview Survey provided the source of our affliction... I mean, our data. We scrutinized surveys, medical records, and the occasional poorly spelled symptom description, all in the pursuit of deciphering the underlying patterns of respiratory distress. It was akin to distinguishing between a wheeze and a cough in a crowded doctor's waiting room – not without its challenges, but ultimately rewarding.

The selection of the study period, spanning from 2000 to 2019, was not a decision made haphazardly. This timeframe encapsulates the ebb and flow of economic tides, from the roaring waves of pre-2008 mortgage fever to the subdued ripples of the post-recession era. It also encompasses the evolution of medical practices and technological advancements that might influence asthma diagnosis and reporting. We aimed to capture the entire asthma-inducing whirlwind of economic and healthcare changes, much like trying to capture a gust of wind in a jar – an endeavor as futile as it is whimsical.

Utilizing a regression analysis approach, we sought to untangle the complex web of relationships between household mortgage expenses and the occurrence of asthma attacks in American children. Our regression model was as robust and stable as a supportive parental figure – providing a safety net for the statistical offspring of our data sets. Our results, much like the elusive punchline of a dad joke, are forthcoming, yet teasingly concealed, waiting to be revealed at the opportune moment for maximum impact.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics 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 US household spending on mortgage interest and charges and Asthma attacks in American children. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children

[[ABSTRACT]]
This research examines the perplexing interplay between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. We delved into the data from the Bureau of Labor Statistics and National Center for Health Statistics to address this wheezy topic. The correlation coefficient of 0.7934694 and p < 0.01 from 2000 to 2019 revealed a strong statistical relationship. Wheeze-economics, indeed!

Our findings suggest a significant positive association between household spending on mortgage interest and charges and the prevalence of asthma attacks in American children. It seems that as household spending on mortgages increases, so does the likelihood of asthma attacks in the younger population. This study illuminates the airway-clear connection between housing expenses and childhood respiratory health. Perhaps we've uncovered the wheeze behind mortgage costs.

This research provides valuable insights into the economic implications of household spending on respiratory health outcomes for children. It calls attention to another dimension of the housing market's impact on public health, showing that there may be more than meets the eye in the intersection of housing economics and children's health. In conclusion, we breathe easier with the knowledge that these findings may inspire further research and policy actions. After all, a good dad joke is like a breath of fresh air!

[[LITERATURE REVIEW]]
The association between US household spending on mortgage interest and charges and the frequency of asthma attacks in American children has been the subject of several comprehensive investigations. Smith et al. (2015) explored the economic implications of housing expenses on children's respiratory health, finding a noteworthy positive correlation between mortgage costs and asthma prevalence. Similarly, Doe and Jones (2018) delved into the National Center for Health Statistics data, revealing a significant link between household spending on mortgages and the occurrence of asthma attacks in the younger population.
While these studies provide important insights into the connection between housing economics and children's health, they fail to unmask the underlying comedic potential of this research question. For a wheeze-free exploration of the economic and medical intersection, it is important to consider the broader body of literature, including non-fiction works such as "The Economics of Respiratory Health in American Children" by Coughlin and "Mortgages and Medical Mysteries" by Wheezelton.
In a parallel vein, fictional sources like "The Wheezy Mortgage Mystery" by Asthma Adams and "The House that Wheezed" by Respira Ryder offer an imaginative exploration of the perplexing interplay between housing expenses and childhood respiratory ailments.
Furthermore, social media posts have recently added a breath of fresh air to this discussion, with one user aptly summarizing the situation as, "Mortgage payments may take our breath away, but they shouldn't take our children's breath, too! #WheezeEconomics #AsthmaAwareness."
As the literature indicates, the connection between US household spending on mortgage interest and charges and asthma attacks in American children is not only a subject of substantive concern but also a rich source of whimsical wordplay. In light of these findings, it is essential to consider the potential for a wheezingly delightful approach to future research in this area. After all, a good dad joke is like a breath of fresh air!

[[RESULTS]]
The results of our study revealed a strong positive correlation between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. The correlation coefficient of 0.7934694 indicated a robust statistical relationship between these variables. It seems that there may be more to "household expenses" than meets the eye - or nose, in this case! It's almost as if mortgages are quite literally taking the breath away from our younger population!
The r-squared value of 0.6295936 demonstrated that a substantial portion of the variance in asthma attacks in American children can be explained by variations in household spending on mortgage interest and charges. It appears that there is a wheezy economic undercurrent to the housing market that has a tangible impact on children's respiratory health. It's as if every mortgage payment comes with a side of wheezing!
Furthermore, the p-value of less than 0.01 indicated that the observed correlation was highly unlikely to be a result of random chance alone. It seems that the relationship between these variables is as real as a child's asthma attack in the middle of the night.
As depicted in Figure 1, the scatterplot illustrates the unmistakable pattern of the strong correlation between household spending on mortgages and the prevalence of asthma attacks in American children. The scatterplot shows a clear and conspicuous trend that leaves little room for doubt regarding the connection between these variables. This relationship is as real as the air we breathe - or struggle to, in the case of asthmatic children!
In summary, our findings provide compelling evidence of a significant positive association between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. This study sheds light on the surprising interplay between housing economics and children's respiratory health, emphasizing the need for further research and policy actions in this wheezy domain.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of this study align with prior research, providing further evidence of the interconnectedness between US household spending on mortgage interest and charges and the prevalence of asthma attacks in American children. The robust statistical relationship we observed supports the findings of Smith et al. (2015) and Doe and Jones (2018), adding another wheeze of insight to the growing body of literature on this topic.

Our analysis revealed a statistical correlation between household spending on mortgages and asthma attacks in children, indicating that as mortgage expenses increase, so does the likelihood of asthma attacks. This association may seem like a real estate of affairs for families with children prone to respiratory issues, as housing costs appear to have a tangible impact on their health. It's as if the market is leaving families breathless in more ways than one!

The r-squared value indicated that a substantial portion of the variance in childhood asthma attacks can be attributed to variations in household spending on mortgage interest and charges. It's like we've uncovered the hidden wheeze-aries in the housing market, where every dollar spent on mortgages comes with an unexpected respiratory cost for some children. Talk about a real estate of affairs!

Furthermore, the p-value underscored the significance of the observed correlation, indicating that this relationship is as unlikely to be due to random chance as finding a needle in a haystack. It's almost as if the statistical evidence has removed any room for doubt, leaving us with a clear understanding of the connection between these variables. This relationship is no statistical wheeze - it's as real as the oxygen we breathe.

The scatterplot visually depicted the unmistakable pattern of the strong correlation, leaving little room for ambiguous interpretations. It's as if the data points were shouting, "Can't you see the correlation? It's as clear as the air on a smog-free day!" The graphical representation supported the numerical findings, further cementing the association between household spending on mortgages and childhood asthma attacks.

In conclusion, these findings contribute to our understanding of the economic implications of housing expenses on children's respiratory health. They highlight the need to consider the wheezy intersection of housing economics and public health, reminding us that when it comes to children's respiratory well-being, there may indeed be more than meets the eye. After all, a good dad joke is like a breath of fresh air!

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics 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 US household spending on mortgage interest and charges and Asthma attacks in American children. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children

[[ABSTRACT]]
This research examines the perplexing interplay between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. We delved into the data from the Bureau of Labor Statistics and National Center for Health Statistics to address this wheezy topic. The correlation coefficient of 0.7934694 and p < 0.01 from 2000 to 2019 revealed a strong statistical relationship. Wheeze-economics, indeed!

Our findings suggest a significant positive association between household spending on mortgage interest and charges and the prevalence of asthma attacks in American children. It seems that as household spending on mortgages increases, so does the likelihood of asthma attacks in the younger population. This study illuminates the airway-clear connection between housing expenses and childhood respiratory health. Perhaps we've uncovered the wheeze behind mortgage costs.

This research provides valuable insights into the economic implications of household spending on respiratory health outcomes for children. It calls attention to another dimension of the housing market's impact on public health, showing that there may be more than meets the eye in the intersection of housing economics and children's health. In conclusion, we breathe easier with the knowledge that these findings may inspire further research and policy actions. After all, a good dad joke is like a breath of fresh air!

[[INTRDUCTION]]
A wise man once said, "Home is where the heart is, but it's also where the wheezing begins." In this study, we embark on an exploration of the curious relationship between US household spending on mortgage interest and charges and the frequency of asthma attacks in American children. As we dive into the data, we'll uncover statistical associations that leave us wheezing for more. Ah, the puns almost write themselves here!
Our research is not just an exercise in financial and medical box-ticking—it's a deep inhalation of the airway-clear connection between housing expenses and childhood respiratory health. We set out to investigate whether there is a wheezy economic downside to the American dream of homeownership. After all, where there's wheeze, there's a way to uncover the surprising connection between mortgage costs and children's health.
The study utilizes data from the Bureau of Labor Statistics and the National Center for Health Statistics, painting a picture that requires no asthma inhaler to grasp. Our investigation extends from 2000 to 2019, offering an expansive view of the relationship between household spending on mortgages and the prevalence of asthma attacks in the younger demographic. So, let's take a deep breath and dive into the data—just be sure to exhale after the joke lands.

[[RESULTS]]
The results of our study revealed a strong positive correlation between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. The correlation coefficient of 0.7934694 indicated a robust statistical relationship between these variables. It seems that there may be more to "household expenses" than meets the eye - or nose, in this case! It's almost as if mortgages are quite literally taking the breath away from our younger population!
The r-squared value of 0.6295936 demonstrated that a substantial portion of the variance in asthma attacks in American children can be explained by variations in household spending on mortgage interest and charges. It appears that there is a wheezy economic undercurrent to the housing market that has a tangible impact on children's respiratory health. It's as if every mortgage payment comes with a side of wheezing!
Furthermore, the p-value of less than 0.01 indicated that the observed correlation was highly unlikely to be a result of random chance alone. It seems that the relationship between these variables is as real as a child's asthma attack in the middle of the night.
As depicted in Figure 1, the scatterplot illustrates the unmistakable pattern of the strong correlation between household spending on mortgages and the prevalence of asthma attacks in American children. The scatterplot shows a clear and conspicuous trend that leaves little room for doubt regarding the connection between these variables. This relationship is as real as the air we breathe - or struggle to, in the case of asthmatic children!
In summary, our findings provide compelling evidence of a significant positive association between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. This study sheds light on the surprising interplay between housing economics and children's respiratory health, emphasizing the need for further research and policy actions in this wheezy domain.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research illuminates the previously unnoticed connection between US household spending on mortgage interest and charges and the prevalence of asthma attacks in American children. It seems that when it comes to housing expenses, the wheeze is more than just the sound of finances escaping! It appears that as household spending on mortgages increases, so does the likelihood of asthma attacks in our younger population. One might say that these findings really take the "breath" away!

Our study provides valuable insights into the economic implications of household spending on respiratory health outcomes for children, revealing an unexpected and potentially far-reaching consequence of the housing market. It seems that the costs of homeownership may extend beyond financial burdens, affecting the very air our children breathe. We might even call this the "Airbnb" effect!

The statistical relationships uncovered in this research call attention to the need for a deeper understanding of the factors contributing to childhood respiratory health. Perhaps it's time to recognize the "asthma-mic" potential of housing economics, and consider how it shapes the well-being of our youngest community members. After all, there's no "puffing" away the significance of these findings!

In summary, our study suggests that a good dad joke is like a breath of fresh air: it leaves everyone feeling better, even if they roll their eyes a little. Given the compelling evidence presented, we assert that no further research is needed in this area. It's time to "exhale" and appreciate the surprising interconnection between housing economics and children's respiratory health.

The findings of this research may provide the impetus for policy actions to address the impact of household spending on respiratory health outcomes for children, ensuring that future generations can breathe a little easier. Thank you, and remember, when it comes to research and statistics, sometimes the "lung" shot pays off!

No further research needed in this wheezy domain.

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 Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Respiratory Economics

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children. Respond only with the name of a fictional organization.

ChatGPT:

National Institute for Family Health and Wellness

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 Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children with an abstract of This research examines the perplexing interplay between US household spending on mortgage interest and charges and the occurrence of asthma attacks in American children. We delved into the data from the Bureau of Labor Statistics and National Center for Health Statistics to address this wheezy topic. The correlation coefficient of 0.7934694 and p < 0.01 from 2000 to 2019 revealed a strong statistical relationship. Wheeze-economics, indeed!
Our findings suggest a significant positive association between household spending on mortgage interest and charges and the prevalence of asthma attacks in American children. It seems that as household spending on mortgages increases, so does the likelihood of asthma attacks in the younger population. This study illuminates the airway-clear connection between housing expenses and childhood respiratory health. Perhaps we've uncovered the wheeze behind mortgage costs.
This research provides valuable insights into the economic implications of household spending on respiratory health outcomes for children. It calls attention to another dimension of the housing market's impact on public health, showing that there may be more than meets the eye in the intersection of housing economics and children's health. In conclusion, we breathe easier with the knowledge that these findings may inspire further research and policy actions. After all, a good dad joke is like a breath of fresh air!

ChatGPT:

asthma attacks, American children, household spending, mortgage interest, Bureau of Labor Statistics, National Center for Health Statistics, correlation coefficient, housing expenses, respiratory health outcomes, public health, housing economics, children's health, research, policy actions

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

US household spending on mortgage interest and charges
Detailed data title: Annual US household spend on mortgage interest and charges, as a percentage of total household spend
Source: Bureau of Labor Statistics
See what else correlates with US household spending on mortgage interest and charges

Asthma attacks in American children
Detailed data title: Percent of girls under 18 in the US who have had an asthma attack in the past year
Source: National Center for Health Statistics
See what else correlates with Asthma attacks in American children

Correlation r = 0.7934694 (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 = 0.6295936 (Coefficient of determination)
This means 63% of the change in the one variable (i.e., Asthma attacks in American children) is predictable based on the change in the other (i.e., US household spending on mortgage interest and charges) over the 20 years from 2000 through 2019.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.0E-5.^Show 0.0000298419203931177900000000
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 0.79 in 0.003% of random cases. Said differently, if you correlated 33,510 random variables^Note You don't actually need 33 thousand variables to find a correlation like this one. 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.

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 19 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 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.

[ 0.54, 0.91 ] 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.

	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
US household spending on mortgage interest and charges (Household spend)	6.93652	7.24227	7.28176	7.23718	6.76576	7.14732	7.75413	7.83674	7.57834	7.32468	6.96543	6.40579	5.96205	6.02348	5.52014	5.10736	5.04092	5.43623	4.53254	4.37845
Asthma attacks in American children (Percent of children)	4.4	5	4.7	4.6	4.1	4.4	4.7	4.8	4.5	4.6	4.7	5.1	4.5	4.4	3.3	3.6	4.2	4.2	3.4	2.7

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([6.93652,7.24227,7.28176,7.23718,6.76576,7.14732,7.75413,7.83674,7.57834,7.32468,6.96543,6.40579,5.96205,6.02348,5.52014,5.10736,5.04092,5.43623,4.53254,4.37845,])
array_2 = np.array([4.4,5,4.7,4.6,4.1,4.4,4.7,4.8,4.5,4.6,4.7,5.1,4.5,4.4,3.3,3.6,4.2,4.2,3.4,2.7,])
array_1_name = "US household spending on mortgage interest and charges"
array_2_name = "Asthma attacks in American children"

# 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 US household spending on mortgage interest and charges
Line chart for only Asthma attacks in American children
The spurious research paper: Breath and Mortgages: The Wheezy Economics of Household Spending on Asthma Attacks in American Children

View another random correlation

How fun was this correlation?

Your correlation inspection deserves a standing ovation!

Correlation ID: 1144 · Black Variable ID: 19963 · Red Variable ID: 1147


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: