In a repeat of how it fumbled its costly and entirely failed COVID app, the UK is ignoring Apple Wallet and will instead develop its own digital wallet for documents such as driving licences.UK ParliamentThe move to digital driving licences, passports, and so on seems so inevitable that Apple has been working on it for years. So it's no surprise that the UK is following the US's lead and implementing the same idea, yet it's not the surprise it should be that the country is going it alone.In the official announcement, the UK government says that it is simplifying digital documents and in doing so to save the equivalent of $55 billion. The UK's economy has yet to recover from 14 years of a Conservative government that split the country from the EU, so saving money is clearly a priority. Continue Reading on AppleInsider | Discuss on our Forums

Ubisoft will release a new trailer for Assassins Creed Shadows on January 23rd, 9 AM PT. Focusing on the story, the thumbnail features Naoe, Yasuke, Junjiro and several unrevealed characters. After the story trailer debuts, there will be two hours of new gameplay from the title on Twitch.Reports have indicated for several weeks that new hands-on previews will drop on the same day. In addition to Ubisofts reveals, there will be impressions from content creators and various publications. Perhaps some more new details are forthcoming, so stay tuned.Assassins Creed Shadows launches on March 20th for Xbox Series X/S, PS5, and PC. Game director Charles Benoit recently outlined several new features surrounding the exploration, from Viewpoints and Safehouses to hiring Scouts to investigate different leads and areas.Check out all the details here. You can also learn more about the Objective Board, which replaces the traditional quest system, and Pathfinder, which helps guide players.

While Mortal Kombat 1 has one more DLC character on the way (Terminator 2s T-1000), there may be another addition to the ninja line-up at some point. Players have recently spotted in-game references to a character named Floyd, as seen in the video below by YouTuber Super.Who is Floyd? In 2022, director Ed Boon said he wanted to create a new pink ninja named Floyd, doubtless a reference to the band Pink Floyd. Hes since referenced the band in a new tweet containing the lyrics to Comfortably Numb (noting it to be the ninja oath).While these recent discoveries could be part of an elaborate Easter egg, a Reddit user discovered assets for Floyd via Mortal Kombat Warehouse. There are pink skins for Sub-Zero and Scorpion, a close-up shot, banners, and more. Before you think he could be a playable character, Floyd may be little more than a secret fight like Reptile from the first Mortal Kombat.Of course, with how the latter turned out, Floyd could be a permanent addition in future titles. Time will tell, so stay tuned.(7 of 7) The ninja oath: There must be some mistake I didnt mean to let them take away my soul Am I too old, is it too late? Ed Boon (@noobde) January 21, 2025

Hand-drawn illustration of two of the seven sampled molars from AustralopithecusDom Jack, MPICThe ape-like human ancestor Australopithecusperhaps best known from the iconic fossil Lucymight not have had much meat on its menu. After examining more than 3.3-million-year-old remains from seven specimens in South Africa, scientists suggest these Australopithecusindividuals were mostly vegetarian.The new work, detailed in a study published last week in the journal Science, sheds light on prehistoric diets using the nitrogen ratios in fossilized teeth.This method opens up exciting possibilities for understanding human evolution, and it has the potential to answer crucial questions, for example, when did our ancestors begin to incorporate meat in their diet? says co-author Alfredo Martnez-Garca, an environmental scientist at the Max Planck Institute for Chemistry, in a statement. And was the onset of meat consumption linked to an increase in brain volume?Scientists suspect that the transition to eating meat allowed our ancestors brains to grow and, consequently, develop the crucial ability to produce and use tools. Exactly when and how that transition happened, however, is still unclear. Lead researcher Tina Ldecke stands beside "Little Foot," anAustralopithecusskeleton thought to be the most complete pre-human skeleton ever found. The specimen was uncovered in the Sterkfontein caves, South Africa, where the studied teeth were also recovered. Bernhard Zipfel / Wits UniversityMeat likely played a significant role in the expansion of cranial capacitylarger brain developmentduring human evolution. Animal resources provide a highly concentrated source of calories and are rich in essential nutrients, minerals and vitamins that are critical for fueling a large brain, study lead author Tina Ldecke, a geochemist at the Max Planck Institute for Chemistry and the University of the Witwatersrand in South Africa, tells Reuters Will Dunham.Ldecke and her colleagues recent work, however, suggests the transition to meat-eating did not happen during the lifetimes of the seven studied Australopithecus individuals, which spanned between 3.3 million and 3.7 million years ago. This conclusion comes despite some evidence that associates some Australopithecus specimens with stone tools.These are still fairly ape-like, small-brained hominins, that already walked upright but had a more ape-like walk, Ldecke tells NPRs Nell Greenfieldboyce. Here, for the first time, we have actual numbers to put on there to say, Ok, not much meat was consumed for these small-brained hominins.The team analyzed nitrogen isotopesforms of nitrogen with different numbers of neutronsin the fossilized tooth enamel from the Australopithecus remains. Because food digestion in animals ultimately expels light nitrogen (14N) from the body, it increases the bodys ratio of heavy nitrogen (15N) to 14N, in comparison to its food. In other words, the higher up the food chain an animal is, the higher its 15N to 14N ratio, according to the statement.Scientists have previously analyzed nitrogen isotope ratios in younger organic remains such as hair, claws and bones, to study human and animal diets. But for the recent research, the team developed a new method to apply this technique to tooth remains that are millions of years old.Tooth enamel is the hardest tissue of the mammalian body and can preserve the isotopic fingerprint of an animals diet for millions of years, Ldecke explains in the statement. The Sterkfontein excavation site, where theAustralopithecusfossils were discovered Dominic StratfordThe researchers then compared the 15N to 14N ratio in the Australopithecus remains to fossilized tooth samples from animals that lived around the same time, including both ancient herbivores and carnivores. Though variable, the Australopithecus ratios were more similar to those of herbivores, ultimately suggesting that these human ancestors depended mostly on a vegetarian diet.This finding, however, doesnt exclude the possibility that Australopithecus feasted on termites, which contain less 15N than large mammal meat does. We see that apes nowadays [fish for termites], so why not our ancestors? says Ldecke to Science News Jake Buehler.Ultimately, the study suggests Australopithecus had not yet started to enjoy a meaty diet at the time. However, the novel method Ldeckes team developed could now be used to track down those human ancestors that did.It means that one can look at other hominins, and try and do the same kind of measurements, and try to get a sense of what they were consuming during life, Bernard Wood, a paleoanthropologist at the George Washington University who was not involved in the study, says to NPR. Moving forward, the researchers plan to continue investigating the origin of meat-eating in our ancestors and whether it triggered an evolutionary benefit.Get the latest stories in your inbox every weekday.Filed Under: Africa, Brain, Chemistry, Evolution, Fossils, Human Evolution, New Research, Paleontology, Primates, Teeth

The helmet was made in the Corinthian style, though historians don't know whether the style actually originated in Corinth. Apollo Art AuctionsA rare ancient Greek helmet is heading to the auction block, where experts expect it to sell for as much as 90,000 (roughly $111,000). Made between 500 and 450 B.C.E., its one of the best preserved specimens to hit the market in recent years, per the lot listing.The bronze artifact is known as a Corinthian helmet, a style thats characterized by almond-shaped eye holes, large cheek-pieces and a wide nose-guard, as Apollo Art Auctions, which is selling the item, writes onFacebook. This particular helmet also sports rows of tiny holes, which would have been used to attach helmet liners via small metal fasteners.The Corinthian style is named forCorinth, an ancient Greekcity-state about 50 miles west of Athens. Beginning around the eighth century B.C.E., Corinth became a commercial hub, thanks to its coastal location, fertile soil and plentiful naturalresources. With time, Corinthians and Athenians developed a bitter commercial rivalry, competing for political and mercantile dominance, which fueled conflict in the region, per Encyclopedia Britannica. Corinth declined in the centuries that followed, and the Roman general Lucius Mummius destroyed it in 146 B.C.E. A century later, Julius Caesar established Corinth as a Roman colonyaccounting for the manyRoman ruins that stand today. Corinth's Temple of Apollo was built around 550 B.C.E. Public domain via Wikimedia CommonsThe city was famous for both its culture and its warfare, writes Live Sciences Tom Metcalfe. However, there is no clear evidence that the Corinthian helmet style was developed in the region. Historians have argued that similar helmets were worn by ancient warriors of many Greek city-states.The artifact going to auction is a rare and exceptionally well-preserved bronze Greek helmet, possibly linked to a Spartan warrior, offering a glimpse into the artistry and craftsmanship of ancient Greece, as Ivan Bonchev, director of Apollo Art Auctions, tells Live Science.The helmet is one of nearly 500 items that will be sold on January 25. Its expected to be the most expensive object in the auctions catalog.Experts say the helmet belonged to ahoplite, a kind of ancient Greek foot soldier who wore heavy armor to shield themselves.The hoplite is one of the quintessential images associated with ancient Greece, as the Greek Reporters Filio Kontrafouri writes. No part of the hoplite panoply is more iconic than his helmet, made even more famous by films like 300 and Troy.These pieces were often lined with cushioning material to protect soldiers heads during combat. Some helmetsparticularly those belonging to members of the elitewere decorated with horse-hair crests, colorful paint and intricate patterns.But Corinthian helmets are best known for their immediately recognizable shape: the domed head, the slightly flaring neck guard, the elongated eye openings, said Hannah Solomon, an ancient art and antiquities specialist at Christies, on the auction houses website last year. Aesthetically, its a beautiful form that has a lyrical nature.Get the latest stories in your inbox every weekday.Filed Under: Ancient Civilizations, Ancient Greece, Ancient Rome, Archaeology, Artifacts, Auctions, Crafts, Greece, History, Roman Empire, Warfare, Weapons

Samsung teased its efforts with multimodal AI and new form factors including smart glasses and extended reality (XR) devices.Read More

Reflector confirms layoffs as Unknown 9: Awakening failed to meet "expectations"It is not clear how many are impacted, but layoffs will also affect back-office staffImage credit: Reflector / Bandai Namco News by Vikki Blake Contributor Published on Jan. 22, 2025 Unknown 9 developer Reflector has notified teams of redundancies following the decision "to not greenlight" an unannounced project.It is not clear how many people have been impacted by the cuts, but the layoffs will also affect back-office staff in order to "bring support teams in line with the single project approach the studio will adopt for the imminent future."Redundant staff will get "adequate" severance packages, and extended health care for themselves and their families. Counselling and "proactive career planning support" will also be available.CEO Herve Hoerdt said proceeding with the project - which had been in the "conceptualisation phase" - "would not have been sustainable for the future of the studio."Reflector, a Bandai Namco studio, explained that "this decision correlates directly with the failure of the studio's ambitious and courageous first project, a new IP with a rich transmedia universe."As its performance did not "come near" to aligning with company expectations despite "numerous timeline adjustments and investments," Hoerdt said the follow-up project "didn't warrant any further exploration."Hoerdt also revealed that where possible, colleagues impacted by the cuts will be reassigned to another unannounced project "based on an existing Badnai Namco IP, which is shaping up very well."

Justin Carter, Contributing EditorJanuary 22, 20252 Min ReadImage via Virtuos.At a GlanceVirtuos' has acquired three studiosUmanaa, Pipeworks, and Abstractionin the name of boosting its production capabilities.Virtuos Studios has gone and acquired three North American and European studios for an undisclosed fee, in what it calls a "new era of collaborative game development."The acquired developers are Umanaa, Pipeworks, and Abstraction, each respectively based out of Canada, the United States, and the Netherlands. All three will provide a "flexible development model for our clients. [...] The expansion integrates the specializations of all three new studios into Virtuos, enabling it to provide clients with comprehensive game production offerings from art to engineering, full game development, and live services as a cohesive unit."With these new teams, Virtuos now has "over 1,200 triple-A caliber" workers spread across its 16 studios. Before this, its most recent acquisition was Third Kind, a co-development studio that worked on Sea of Thieves. It's also opened studios in Tokyo and Prague, and closed down its Calypte subsidiary, which existed for only a year.Meet the new Virtuos studiosVirtuos' announcement gives a brief rundown of the new studios under its umbrella, and their individual (and sizable) recent works. All three will continue to be led by their respective management teams, and CEO Gilles Langourieux said the acquisitions help "significantly augment our creative development capabilities...around the world."Founded in 1999, the 25-year-old Pipeworks has helped with live-service and online titles such as Concord, Ara: History Untold, and several Call of Duty and football-related EA titles. The team will lead Virtuos' development operations in the United States, which also stretch northward to its subsidiaries, Beyond-FX, CounterPunch, and Virtuos Montreal.Abstraction, which opened in 2007, has worked on "over 200 games," including Baldur's Gate 3, Halo: Master Chief Collection, and Dune: Awakening. Virtuos will use it to accelerate its internal engineering network Virtuos Labs, which "provides our clients with advanced engineering solutions in commercial and proprietary engines."Finally, the three-year-old Umanaa, which has helped with Ubisoft's For Honor and Assassin's Creed games, has been chosen to "drive innovation" with its own Virtuos Originals game. The initiative started in 2023, and sees internal teams create their own original projects that will enter full production if Virtuos can secure a publishing deal for them.Read more about:M&AAbout the AuthorJustin CarterContributing Editor, GameDeveloper.comA Kansas City, MO native, Justin Carter has written for numerous sites including IGN, Polygon, and SyFy Wire. In addition to Game Developer, his writing can be found at io9 over on Gizmodo. Don't ask him about how much gum he's had, because the answer will be more than he's willing to admit.See more from Justin CarterDaily news, dev blogs, and stories from Game Developer straight to your inboxStay UpdatedYou May Also Like

Bagel is a novel AI model architecture that transforms open-source AI development by enabling permissionless contributions and ensuring revenue attribution for contributors. Its design integrates advanced cryptography with machine learning techniques to create a trustless, secure, collaborative ecosystem. Their first platform, Bakery, is a unique AI model fine-tuning and monetization platform built on the Bagel model architecture. It creates a collaborative space where developers can fine-tune AI models without compromising the privacy of their proprietary resources or exposing sensitive model parameters.Origin and VisionThe idea for Bagel emerged from its founder, Bidhan Roy, who has a rich engineering and machine learning background and has contributed to the worlds largest ML infrastructures at Amazon Alexa, Cash App, and Instacart. Recognizing the unsustainability of open-source AI as a charitable model, Roy envisioned a system that would incentivize contributors by making their work monetizable. His introduction to cryptography during his work on Cash Apps Bitcoin trading platform in 2017 became the foundation for Bagels innovative approach to combining cryptographic methods with AI development.Bagels unique value proposition is built around three core pillars:Attribution: Bagel ensures that every structural or parametric contribution is verifiably attributed using its novel ZKLoRA method, providing a transparent trail of creative work and fostering accountability in collaborative AI development.Ownership: Contributors retain perpetual claims on their innovations through privacy-preserving containers and parameter obfuscation, eliminating the need for traditional licensing agreements while safeguarding intellectual property.Privacy: Secure model encapsulation and layered obfuscation protect proprietary components, preventing unauthorized access even in untrusted or outsourced compute environments, ensuring privacy and trust throughout the development process.Core Innovations of BagelPermissionless Contributions: Bagel allows developers, researchers, and resource owners to contribute to AI model development without requiring explicit permissions or prior agreements. This decentralized approach eliminates barriers to entry.Revenue Attribution: Bagels unique feature is its ability to attribute and distribute revenue to all ecosystem contributors fairly. The platform accurately tracks contributions and model enhancements using cryptographic techniques, ensuring that contributors are rewarded proportionately.Cryptography Meets Machine Learning:Parameter-Efficient Fine-Tuning (PEFT): It optimizes model fine-tuning processes, reducing resource requirements while maintaining performance.ZKLoRA: Bagel Research Teams latest innovation a zero-knowledge protocol that verifies LoRA updates for base model compatibility without exposing proprietary data, ensuring secure and efficient collaboration.Bagels architecture is implemented through its platform, Bakery. It enables decentralized AI development by allowing developers to contribute models and optimizations securely, dataset providers to share proprietary data privately using cryptographic methods, and resource owners to offer computational power while retaining control and privacy. In Bakery, multiple contributors can participate in building AI models:A contributor may supply a base model.A third party could offer GPU resources from a remote location.Now, lets look into their latest research on ZKLoRA. In this research, the Bagel Research Team focuses on enabling efficient and secure verification of Low-Rank Adaptation (LoRA) updates for LLMs in distributed training environments. Traditionally, fine-tuning these models involves external contributors providing LoRA updates, but verifying that these updates are genuinely compatible with the base model while protecting proprietary parameters poses challenges.Existing methods, such as rerunning a forward pass or manually inspecting large parameter sets, are computationally infeasible, especially for models with billions of parameters. Contributors proprietary LoRA weights must also be protected, while base model owners must verify the accuracy and validity of the updates. This creates a dual challenge: mAIntaining trust in decentralized and collaborative AI development while preserving intellectual property and computational efficiency. The lack of a robust and efficient verification mechanism for LoRA updates limits their scalability and secure use in real-world applications.To address the challenge mentioned above, the Bagel Research Team introduced ZKLoRA. This zero-knowledge protocol combines cryptographic methods with fine-tuning techniques to ensure the secure verification of LoRA updates without exposing private weights. ZKLoRA employs zero-knowledge proofs, polynomial commitments, and succinct cryptographic designs to verify LoRAs compatibility with base models efficiently. This innovation allows LoRA contributors to protect their intellectual property while enabling base model users to validate updates confidently.The ZKLoRA protocol operates through a structured process. First, the base model user provides partial activations by running unaltered model layers. These partial activations are then used by the LoRA owner, who applies their proprietary updates and constructs a zero-knowledge proof. This proof ensures that the LoRA updates are valid and compatible with the base model without disclosing proprietary information. Verification, which takes just 12 seconds per module, ensures the integrity of each LoRA update, even for models with billions of parameters. For example, a 70-billion parameter model with 80 LoRA modules can be verified in only a few minutes. This efficiency makes ZKLoRA a scalable solution for conditions requiring frequent or large-scale compatibility checks.Also, ZKLoRA was rigorously evaluated across various LLMs, including models like distilgpt2, Llama-3.3-70B, and Mixtral-8x7B. The researchers analyzed the total verification time, proof generation time, and settings time of the number of LoRA modules and their average parameter sizes. Results showed that even with higher LoRA counts, the increase in verification time was modest due to the succinct nature of ZKLoRAs design. For instance, a model with 80 LoRA modules required less than 2 seconds per module for verification, while total proof generation and settings time, though dependent on module size, remained manageable. This demonstrates ZKLoRAs capability to handle multi-adapter scenarios in large-scale deployments with minimal computational overhead.The research highlights several key takeaways that underscore ZKLoRAs impact:The protocol verifies LoRA modules in just 12 seconds, even for models with billions of parameters, ensuring real-time applicability.ZKLoRA scales efficiently with the number of LoRA modules, maintaining manageable proof generation and verification times.By integrating cryptographic techniques like zero-knowledge proofs and differential privacy, ZKLoRA ensures the security of proprietary LoRA updates and base models.The protocol enables trust-driven collaborations across geographically distributed teams without compromising data integrity or intellectual property.With minimal computational overhead, ZKLoRA is suitable for frequent compatibility checks, multi-adapter scenarios, and contract-based training pipelines.In conclusion, Bagel has transformed decentralized AI development through its innovative platform, Bakery, and the ZKLoRA protocol. They have addressed critical challenges in fine-tuning LLMs, such as verifying LoRA updates securely and efficiently while preserving intellectual property. Bagel has also provided a robust framework for trust-driven collaboration. Bakery enables open-source contributors to monetize their work effectively. At the same time, ZKLoRA leverages advanced cryptographic techniques like zero-knowledge proofs and differential privacy to ensure secure and scalable compatibility checks. With verification times as short as 12 seconds per module, even for multi-billion parameter models, ZKLoRA demonstrates remarkable efficiency and makes it a practical solution for real-world applications. Finally, Bakery is the first product to utilize the Bagel model architecture. This architecture represents a core primitive that can be leveraged by future products developed by the Bagel team and other companies aiming to innovate in the open-source AI space.Sources:Thanks tothe Bagel AI teamfor the thought leadership/ Resources for this article.Bagel AI team has supported us in this content/article. Asif RazzaqAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences. Meet 'Height':The only autonomous project management tool (Sponsored)

Sleepless Nights: A Statistical Look at Modern Sleep Patterns 0 like January 22, 2025Share this postAuthor(s): Daksh Trehan Originally published on Towards AI. A Statistical Journey Into the Factors That Shape Our SleepThey say the best cure for sleepless nights is to count sheeps. But for some of us, the sheeps are too busy scrolling social media to help!This playful opening reflects a deeper truth about modern life: weve filled our days with far more than our ancestors ever did. Unlike them, we now juggle endless distractions, social media pressures, and other modern-life traps. These factors leave our minds overstimulated and restless, with little space for mental peace.This clutter has tangible consequences. Overstimulated and overwhelmed, our minds struggle to find peace, resulting in sleepless nights spent doom-scrolling instead of recharging. Sleep a basic, natural necessity is now increasingly elusive in our ever-connected world.In this article, well explore the patterns and reasons behind sleepless nights using real-world data and advanced statistical techniques. By understanding the factors that disrupt our sleep from stress to health and lifestyle choices.Note: The purpose of this article is purely educational. We are analyzing available data to demonstrate statistical techniques engagingly. This article is not intended to provide medical advice, as I am neither a medical professional nor practicing in the medical field. While good sleep can enrich lives, this article simply seeks to explore the data for insights. Rest assured, there is no judgment if restful sleep isnt always a priority for you.Content Table:What can be regarded as good sleep? And why is it important?Understanding our Data.Possible information insights.Advanced Analytics: Validating Assumptions and Discovering PatternsWhat can be regarded as good sleep? And why is it important?Good sleep isnt just about the number of hours you spend in bed its about the quality of those hours. The National Sleep Foundation recommends 79 hours for adults, but what defines good sleep goes beyond duration. Key indicators of good sleep include:Sleep Efficiency: This measures how much of your time in bed is spent sleeping. A high sleep efficiency means you fall asleep quickly and stay asleep.Continuity: Uninterrupted sleep cycles allow your body to go through all the restorative phases of sleep, enabling physical and mental recovery.Feeling Rested: Waking up feeling refreshed and alert is a sign of good sleep, regardless of duration.The Consequences of Poor SleepThe absence of good sleep can have a significant impact on both the body and mind, leading to short-term and long-term consequences:Impaired Cognitive Function: Poor sleep disrupts memory, concentration, and decision-making abilities. Over time, this can affect work performance and learning capacity.Emotional Instability: Sleep deprivation increases irritability, anxiety, and susceptibility to stress. It also impairs emotional regulation, which may lead to strained relationships.Weakened Immune System: Without adequate sleep, the body struggles to fight off infections and heal properly, making individuals more susceptible to illnesses.Hormonal Imbalances: Poor sleep affects hormones that regulate hunger, stress, and growth, contributing to weight gain and metabolic issues.Decreased Longevity: Studies have shown that chronic lack of sleep can reduce life expectancy due to its cumulative effects on health.Sleep is a cornerstone of physical and mental well-being, and the repercussions of poor sleep should not be underestimated. In the next section, well dive into the data weve gathered to understand modern sleep patterns and what might be disrupting our ability to achieve this fundamental need.Understanding our DataThe data is sourced from NPHA(National Poll on Healthy Aging)[1]. It includes variables that capture individual health, lifestyle, and sleep-related habits. Heres a snapshot of what our data contains:Demographic Information: Age, gender, and employment status of individuals.Health Indicators: Physical and mental health ratings, stress levels, and whether any medical conditions affect sleep.Sleep-Related Factors: Duration of sleep, frequency of trouble sleeping, and the use of sleep medications.Lifestyle Variables: Presence of daily habits like screen time, caffeine consumption, and exercise.The data consists of approximately 700 observations, making it a robust sample to analyze. By combining statistical techniques with these variables, we aim to uncover patterns and relationships that reveal what factors contribute to sleepless nights.Possible Information InsightsThere are several possible statistical ways to uncover basic insights from our data:Descriptive StatisticsThese are a set of tools and techniques used to summarize, describe, and organize data in a meaningful way. They help you understand the basic features of your dataset by providing a summary of its key characteristics. Descriptive statistics are often the first step in analyzing data because they give you an overview of what the data looks like.Types of Descriptive Statistics, Source1. Measures of Central Tendency: These describe the center or typical value in a dataset.Mean: The average of all values.Median: The middle value when the data is ordered.Mode: The most frequently occurring value.2. Measures of Dispersion(Spread): These describe how spread out or dispersed the data values are.Range: The difference between the maximum and minimum values.Variance: The average squared deviation from the mean.Standard Deviation: The square root of variance, indicating how far values typically are from the mean.Interquartile Range (IQR): The range between the 25th and 75th percentiles, showing the spread of the middle 50% of the data.3. Frequency Distributions: Show how often each value or category occurs in the dataset.Frequency counts for categorical data.Histograms or bar charts to visualize distributions.4. Shape of the Distribution: Describes how data is distributed.Skewness: Indicates whether data is symmetric or skewed to the left or right.Kurtosis: Measures whether the data is flat or peaked relative to a normal distribution.Why Are Descriptive Statistics Important?Simplification: They summarize large datasets into manageable numbers or visuals.Understanding Trends: They help identify patterns, trends, and outliers in the data.Data Cleaning: Descriptive statistics help detect missing data, errors, or anomalies.Foundation for Advanced Analysis: They lay the groundwork for inferential statistics and predictive modeling by providing a clear understanding of the dataset.Descriptive Statistics on Our Data1. Doctors Visited Category:Most individuals fall into the Low or Medium categories, indicating infrequent doctor visits.Those in the High category may represent individuals with chronic conditions or persistent sleep-related issues.2. Age Groups (if categorized):Younger individuals might report fewer sleep-related issues compared to middle-aged or senior groups, who are more likely to seek medical help or face sleep troubles.3. Stress and Trouble Sleeping:High-stress levels are frequently associated with trouble sleeping categories, supporting earlier observations about stress being a major sleep disruptor.4. Usage of Sleep Aids:Prescription sleep medication usage is more prevalent among individuals reporting higher trouble sleeping categories, indicating dependency trends.5. Demographics and Sleep:Employment status (e.g., unemployed or retired individuals) and gender distributions might reveal disparities in stress levels and sleep-related issues.CorrelationCorrelation is a statistical measure that describes the strength and direction of the relationship between two numerical variables.Positive Correlation: As one variable increases, the other variable also increases. e.g. Stress levels and trouble sleeping.Negative Correlation: As one variable increases, the other variable decreases. e.g. Physical health and trouble sleeping.No Correlation: No relationship between the variables. e.g. Race and prescription sleep medication usage might have no relationship (depending on your data).Types of Correlation, Source1. Descriptive Measures Help Identify Trends:Using descriptive statistics like the mean or standard deviation, you can understand the overall patterns in your variables (e.g., average stress level, variability in trouble sleeping).Correlation then allows you to see how these patterns interact between variables (e.g., does higher stress correlate with more trouble sleeping?).2. Highlight Relationships in Group Data:For instance, descriptive statistics might show that individuals with poor health ratings have higher stress levels. Correlation would quantify that relationship.3. Validate Insights from Descriptive Statistics:If descriptive stats suggest a trend (e.g., older individuals report more trouble sleeping), correlation can confirm if age is significantly related to trouble sleeping.Based on our dataset, the correlation matrix looks like this:Key Insights:1. Stress and Sleep Trouble:A high positive correlation between Stress Keeps Patient from Sleeping and Trouble Sleeping suggests that stress is a major contributor to sleep problems.Insight: Stress management interventions could directly improve sleep quality.2. Physical Health and Trouble Sleeping:A negative correlation between Physical Health and Trouble Sleeping indicates that better physical health is associated with fewer sleep-related issues.Insight: Encouraging physical well-being could lead to better sleep outcomes.3. Dependence on Sleep Aids:A moderate positive correlation between Trouble Sleeping and Prescription Sleep Medication shows that individuals with frequent trouble sleeping are more likely to rely on medication.Insight: Identifying non-medical interventions might reduce dependency on prescription sleep aids.4. Pain and Sleep:A moderate positive correlation between Pain Keeps Patient from Sleeping and Trouble Sleeping highlights the role of physical discomfort in disrupting sleep.Insight: Pain management strategies could alleviate sleep issues in affected individuals.5. Mental Health and Stress:A moderate negative correlation between Mental Health and Stress Keeps Patient from Sleeping suggests that poor mental health is linked to higher stress levels, which in turn affect sleep.Insight: Addressing mental health concerns could reduce stress and improve sleep.6. Bathroom Needs and Sleep Disruption:A smaller positive correlation between Bathroom Needs Keeps Patient from Sleeping and Trouble Sleeping indicates that frequent bathroom visits moderately affect sleep quality.Insight: This could highlight specific conditions (e.g., bladder or prostate issues) that require attention.7. Age and Health:If a variable like Age correlates negatively with Physical Health, it suggests that older individuals may experience poorer physical health, contributing to sleep challenges.Insight: Targeted interventions for older populations might improve overall health and sleep.8. Low Correlations:Variables like Race and Gender show weak or negligible correlations with sleep-related factors.Insight: These demographic factors may not significantly influence sleep patterns in this dataset.Advanced Analytics: Validating Assumptions and Discovering PatternsAre stress levels truly the strongest predictor of trouble sleeping? Do age and health ratings influence sleep quality as much as we suspect? To answer these questions, we turn to hypothesis testing and regression analysis.Hypothesis TestingHypothesis testing allows us to validate key assumptions, such as whether high-stress levels significantly correlate with frequent trouble sleeping.e.g., we can test the hypothesis that poor physical health increases reliance on prescription sleep medication or that age significantly impacts sleep quality. These tests help us determine which relationships are statistically significant, providing confidence in our findings.It provides clear evidence of relationships by distinguishing between random patterns and genuine associations. This makes it particularly valuable in ensuring that our conclusions are not influenced by noise in the data. By identifying statistically significant patterns, we can confidently guide decisions based on evidence rather than assumptions.Null Hypothesis: This is the default assumption that there is no effect or no difference. It represents the status quo.Alternative Hypothesis: This is the claim that we are testing, which suggests that there is an effect or a difference.The goal of hypothesis testing is to determine whether the observed data provides enough evidence to reject the null hypothesis in favor of the alternative hypothesis.Step-by-Step Hypothesis TestingTypes of Hypothesis TestingBased on the insights we uncovered in the previous paragraph, lets try to validate a few of the claims:Sleep is impacted by StressAssumptions:The data is categorical (e.g., Yes or No for stress and trouble sleeping).Observations are independent (e.g., each persons response is unrelated to anothers).The counts in each group of the table should not be too small (ideally at least 5 in each group) for the test to work properly.Hypothesis TestingThe Chi-Square test is specifically designed to evaluate relationships between two categorical variables.Our goal is to determine whether the presence of stress is associated with trouble sleeping.Results:The test statistic (2) was 22.18, and the p-value was 0.000002.Since the p-value < 0.05, we rejected the null hypothesis, concluding that stress significantly impacts trouble sleeping.2. Do we have a relationship between bad physical health & poor sleep?Assumptions:The physical health score is continuous and normally distributed in the population.The amount of variation in the two groups (those with and without trouble sleeping) should be about the same, or we need to adjust the test to handle differences.Observations are independent (no repeated measures).Hypothesis Testing?The t-test is appropriate for comparing the means of a continuous variable (physical health score) between two groups.We want to see if individuals reporting trouble sleeping had significantly lower physical health scores than those who didnt.Results:The test statistic (t) was -4.74, and the p-value was 0.000003.Since the p-value < 0.05, we rejected the null hypothesis, concluding that physical health significantly correlates with trouble sleeping. Specifically, poor physical health is associated with more sleep issues.3. Does Employment Status Impact Trouble Sleeping?Assumptions:The variables are categorical: Employment status (e.g., employed, retired) and trouble sleeping (binary) are both categorical.Observations are independent.Expected frequencies in the contingency table are at least 5:Ensures the test is reliable.Hypothesis Testing?Similar to stress, employment status and trouble sleeping are categorical variables, making the Chi-Square test suitable for checking their relationship.We want to see if the employment status of individuals can impact a good night's sleep.Result:The test statistic (2) is 7.93, and the p-value was 0.0470.Since the p-value < 0.05, we rejected the null hypothesis, concluding that employment status can significantly correlate with trouble sleeping.Regression AnalysisRegression analysis goes a step further by quantifying the impact of variables on sleep quality.For instance, using regression models, we can measure how much stress contributes to trouble sleeping or how changes in physical health influence the likelihood of using sleep aids. These methods provide actionable insights, allowing us to prioritize interventions for the factors most strongly affecting sleep.Define the ModelDependent Variable (Target): Trouble Sleeping (binary: 1 = Often/Sometimes, 0 = No)Independent Variables (Predictors):Stress (binary: Yes = 1, No = 0), Employment status (categorical, one-hot encoded), Physical Health (ordinal: Poor = 0, Fair = 1, Good = 2, Very Good = 3), Age group (ordinal: 1824 = 1, 2544 = 2, etc.), Gender (binary: Male = 1, Female = 0), Pain and bathroom needs at night (binary: Yes = 1, No = 0)2. Choose the Regression TypeSince trouble sleeping is a binary variable (0 or 1), well use logistic regression[2], which predicts the probability of an outcome falling into one of the two categories.3. Check Assumptions of Logistic RegressionIndependence of observations: Each row in the dataset should represent a unique individual.No multicollinearity: Independent variables should not be highly correlated with each other.Linearity of independent variables: For logistic regression, continuous predictors should have a linear relationship with the log odds of the dependent variable.4. Fit the ModelWell fit a logistic regression model to the data and evaluate:Coefficients: Show the direction and strength of each predictors relationship with trouble sleeping.P-values: Indicate whether each predictor is statistically significant (p<0.05p < 0.05p<0.05).Model Performance: Evaluate metrics like accuracy, precision, recall, and the Area Under the ROC Curve (AUC).The blue curve represents the models performance, showing the trade-off between the true positive rate (sensitivity) and the false positive rate.The red dashed line represents a random guess (no predictive power).The closer the blue curve is to the top-left corner, the better the model performs. The Area Under the Curve (AUC) value of 70.41% indicates a moderately good ability to distinguish between individuals with and without trouble sleeping.Plotting Coefficient Values with Predictor Variables gives us the following insights:5. Influential PredictorsThe regression coefficients (displayed in the analysis) highlight which variables have the strongest influence on trouble sleeping:Stress: A strong positive predictor, confirming that individuals experiencing stress are significantly more likely to report trouble sleeping.Employment Status: Employment categories (e.g., employed, retired, unemployed) showed varying impacts, reflecting differences in stress levels and routines that affect sleep.Physical Health: Poor physical health is associated with a higher likelihood of trouble sleeping, consistent with earlier analyses.Pain and Bathroom Needs at Night: These also emerged as positive predictors, suggesting that physical discomfort or disruptions significantly impact sleep.Gender: Gender differences were accounted for, but their impact was less pronounced than stress and physical health.Age Group: Age had a relatively weaker effect, aligning with earlier findings that age alone is not a significant determinant of trouble sleeping.Looking AheadThe findings from this analysis can inform targeted interventions to improve sleep quality:Stress reduction programs and workplace wellness initiatives can mitigate sleep problems, particularly among employed individuals.Customized strategies for retirees and unemployed individuals could focus on maintaining consistent routines and addressing physical health challenges.Addressing physical discomfort and nighttime disruptions can provide immediate relief for those experiencing trouble sleeping.As always, thank you so much for reading, and please share this article if you found it useful! References:[1] National Poll on Healthy Aging (NPHA) [Dataset]. (2017). UCI Machine Learning Repository. https://doi.org/10.3886/ICPSR37305.v1.[2] Logistic Regression Explained[3] Code: Sleepless Nights: A Statistical Look at Modern Sleep PatternsFind me on the Web: www.dakshtrehan.comConnect with me at LinkedIn: www.linkedin.com/in/dakshtrehanRead my Tech blogs: www.dakshtrehan.medium.comCheers!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post