#regression analysis multiple regression data analysis

[romanticize-until-you-drop]

So I just won a competition for my research project…

MOM DAD IM A REAL SCIENTIST!!

[olderthannetfic]

Looking at what translations are on shelves is interesting.

It's hard to get solid numbers, but maybe 3-4% of books coming out in the US are translations from other languages. In other countries, it sees to be in the low double digits for some languages but potentially much higher.

A thing I've noticed personally is that fun genre fiction seems to be particularly likely to be translated from English in other places and almost never a translation here in the US unless we're looking at manga or the recent wave of danmei novel translations. Readers of literary fiction are a little more open to translated works for multiple reasons, including that literary fiction publishers hire translators who don't suck.

The UK, reportedly, has more translations on shelves, though the entire Anglophone world is prone to these problems.

Here's an example of the sort of discussion I've found while searching just now.

3 percent, 60 percent: the singularity of English in the world of translations

making sense of some crude numbers

The point is: a regression analysis of the available data seems to suggest that in this translation universe, the tendency for the percentage of translations is to decrease with the number of publications in that language. (More precisely: the percentage of translations decreases with the increase in the share of the language of total world-wide publications.) Given the high number of English language publications, a low percentage of translations into English is to be expected. The disappointing volume and low visibility of translation into English correlate with the global dominance of English language publishing, and might be seen as its effect.

Or, put more simply, English speakers are well-fed, so what do we need translations for?

When we encounter an area where we're not producing enough at home, we start translating, but that usually takes a fad for xianxia novels or something.

[covid-safer-hotties]

BNT162b2 XBB Vaccine for COVID-19 Among Children 5-17 Years of Age

This case-control study of US children examines vaccine effectiveness rates of a messenger RNA vaccine during the 2023-2024 respiratory viru

Reference saved in our archive

An excellent breakdown of the efficacy of covid vaccines in children with some haunting statistics included:

Finally, only 7.4% of children ages 5 to 17 years in our study had received an XBB vaccine by the end of April 2024, which is similar to California Department of Public Health estimates of 6% to 7% for this same age group and time period.

Even if we could actually vax our way out of the covid pandemic, there's nowhere near enough uptake to pretend like we can. Mask up. Clean the air. Take all the steps and precautions you can to help keep yourself and everyone around you safe and healthy.

Introduction Vaccine effectiveness of XBB messenger RNA COVID-19 vaccines against mild to severe outcomes has been shown for adults in multiple global settings.1-3 However, data describing the effectiveness of updated COVID-19 vaccines in children are scarce. We evaluated the BNT162b2 XBB vaccine (Pfizer-BioNTech) effectiveness in children ages 5 to 17 years during the 2023-2024 respiratory virus season in a large integrated US health system.

Methods Similar to prior studies,3 we conducted a test-negative case-control analysis to evaluate BNT162b2 XBB vaccine effectiveness against acute respiratory infection (ARI)-associated (eTable in Supplement 1) hospital admission and emergency department (ED) or urgent care visits among children ages 5 to 11 (using a 10-μg formulation) and 12 to 17 (30-μg formulation) years from October 10, 2023, through April 30, 2024, at Kaiser Permanente Southern California (KPSC). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. This study was approved by the KPSC institutional review board with a waiver of informed consent because it involved a minimal risk to study participants.

Cases had a positive SARS-CoV-2 polymerase chain reaction (PCR) or antigen test during a hospital admission or ED or urgent care visit, and controls tested negative and had no evidence of a positive SARS-CoV-2 test in the prior 90 days. In sensitivity analyses, we evaluated the impact of (1) restricting results to PCR testing only, and (2) excluding test-negative controls identified by antigen testing alone. Odds ratios (ORs) and 95% CIs comparing the odds of vaccination with 1 or more doses of BNT162b2 XBB with the odds of not being vaccinated with any XBB vaccine were calculated from multivariable logistic regression models (eMethods in Supplement 1). Vaccine effectiveness was calculated as 1 − OR multiplied by 100%. Significance was determined with 95% CIs.

Results Of 15 233 ARI encounters among children ages 5 to 17 years meeting eligibility criteria (7342 female [48.2%]; 1450 Asian or Pacific Islander [9.5%], 1358 Black [8.9%], 8752 Hispanic [57.5%], 2678 White [17.6%]), 9834 were among 5-to-11-year–olds (334 hospital admissions [3.4%], 4377 ED visits [44.5%], and 5123 urgent care visits [52.1%]) and 5399 were among 12-to-17-year–olds (125 hospital admissions [2.3%], 1953 ED visits [36.2%], and 3321 urgent care visits [61.5%]) (eFigure in Supplement 1). Overall, 336 of 9834 5-to-11-year–olds (3.4%) and 388 of 5399 12-to-17-year–olds (7.2%) tested SARS-CoV-2 positive (Table 1). In the full cohort, 1125 children (7.4%) received an XBB vaccine by the end of April 2024; 339 of 9834 5-to-11-year–olds (3.4%) and 264 of 5399 12-to-17-year–olds (4.9%) received BNT162b2 XBB vaccine with median time since vaccination of 75 days (range, 15-199 days) and 64.5 days (range, 16-197 days), respectively. Among those ages 5 to 11 and 12 to 17 years, estimated adjusted BNT162b2 XBB vaccine effectiveness was 68% (95% CI, 11%-88%) and 63% (95% CI, 20%-83%) against COVID-19–associated hospital admission or ED or urgent care visits, respectively, with an overall estimated vaccine effectiveness for all 5-to-17-year–olds of 65% (95% CI, 36%-81%) (Table 2). No COVID-19–associated hospitalizations occurred among those who received BNT162b2 XBB vaccine. Estimated vaccine effectiveness was generally similar for all age groups if results were restricted only to PCR test results or if antigen results were used only to identify cases (Table 2).

Discussion Our results suggest that the BNT162b2 XBB vaccine provided protection against COVID-19–associated hospitalization and ED or urgent care visits among children 5 to 17 years of age during the 2023-2024 season with estimated vaccine effectiveness point estimates ranging from 63% to 73%. The US rate of COVID-19–associated hospitalization was roughly 10.5 per 100 000 among 5-to-17-year–olds from October 2023 through April 2024.4 Assuming 65% vaccine effectiveness, vaccinating the roughly 54.3 million 5-to-17-year–olds in the US5 could have averted approximately 3700 hospitalizations and, using the approximately 30:1 ratio of hospitalizations to ED or urgent care visits we observed in our study, roughly 111 000 ED or urgent care visits during the 2023-2024 respiratory virus season. Our observational design has previously described limitations,3 including potential unmeasured confounding, potential misclassification of prior infection or whether ARI encounters were truly COVID-19–related, and lack of generalizability to other populations. Finally, only 7.4% of children ages 5 to 17 years in our study had received an XBB vaccine by the end of April 2024, which is similar to California Department of Public Health estimates of 6% to 7% for this same age group and time period.6 More efforts are needed to improve pediatric COVID-19 vaccine uptake.

[tech-insides]

What are the skills needed for a data scientist job?

It’s one of those careers that’s been getting a lot of buzz lately, and for good reason. But what exactly do you need to become a data scientist? Let’s break it down.

Technical Skills

First off, let's talk about the technical skills. These are the nuts and bolts of what you'll be doing every day.

Programming Skills: At the top of the list is programming. You’ll need to be proficient in languages like Python and R. These are the go-to tools for data manipulation, analysis, and visualization. If you’re comfortable writing scripts and solving problems with code, you’re on the right track.

Statistical Knowledge: Next up, you’ve got to have a solid grasp of statistics. This isn’t just about knowing the theory; it’s about applying statistical techniques to real-world data. You’ll need to understand concepts like regression, hypothesis testing, and probability.

Machine Learning: Machine learning is another biggie. You should know how to build and deploy machine learning models. This includes everything from simple linear regressions to complex neural networks. Familiarity with libraries like scikit-learn, TensorFlow, and PyTorch will be a huge plus.

Data Wrangling: Data isn’t always clean and tidy when you get it. Often, it’s messy and requires a lot of preprocessing. Skills in data wrangling, which means cleaning and organizing data, are essential. Tools like Pandas in Python can help a lot here.

Data Visualization: Being able to visualize data is key. It’s not enough to just analyze data; you need to present it in a way that makes sense to others. Tools like Matplotlib, Seaborn, and Tableau can help you create clear and compelling visuals.

Analytical Skills

Now, let’s talk about the analytical skills. These are just as important as the technical skills, if not more so.

Problem-Solving: At its core, data science is about solving problems. You need to be curious and have a knack for figuring out why something isn’t working and how to fix it. This means thinking critically and logically.

Domain Knowledge: Understanding the industry you’re working in is crucial. Whether it’s healthcare, finance, marketing, or any other field, knowing the specifics of the industry will help you make better decisions and provide more valuable insights.

Communication Skills: You might be working with complex data, but if you can’t explain your findings to others, it’s all for nothing. Being able to communicate clearly and effectively with both technical and non-technical stakeholders is a must.

Soft Skills

Don’t underestimate the importance of soft skills. These might not be as obvious, but they’re just as critical.

Collaboration: Data scientists often work in teams, so being able to collaborate with others is essential. This means being open to feedback, sharing your ideas, and working well with colleagues from different backgrounds.

Time Management: You’ll likely be juggling multiple projects at once, so good time management skills are crucial. Knowing how to prioritize tasks and manage your time effectively can make a big difference.

Adaptability: The field of data science is always evolving. New tools, techniques, and technologies are constantly emerging. Being adaptable and willing to learn new things is key to staying current and relevant in the field.

Conclusion

So, there you have it. Becoming a data scientist requires a mix of technical prowess, analytical thinking, and soft skills. It’s a challenging but incredibly rewarding career path. If you’re passionate about data and love solving problems, it might just be the perfect fit for you.

Good luck to all of you aspiring data scientists out there!

[religion-is-a-mental-illness]

Screen use in transgender and gender-questioning adolescents: Findings from the Adolescent Brain Cognitive Development (ABCD) Study

To assess the association between transgender or gender-questioning identity and screen use (recreational screen time and problematic screen

Abstract

Objective: To assess the association between transgender or gender-questioning identity and screen use (recreational screen time and problematic screen use) in a demographically diverse national sample of early adolescents in the U.S.

Methods: We analyzed cross-sectional data from Year 3 of the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®, N = 9859, 2019-2021, mostly 12-13-years-old). Multiple linear regression analyses estimated the associations between transgender or questioning gender identity and screen time, as well as problematic use of video games, social media, and mobile phones, adjusting for confounders.

Results: In a sample of 9859 adolescents (48.8% female, 47.6% racial/ethnic minority, 1.0% transgender, 1.1% gender-questioning), transgender adolescents reported 4.51 (95% CI 1.17-7.85) more hours of total daily recreational screen time including more time on television/movies, video games, texting, social media, and the internet, compared to cisgender adolescents. Gender-questioning adolescents reported 3.41 (95% CI 1.16-5.67) more hours of total daily recreational screen time compared to cisgender adolescents. Transgender identification and questioning one's gender identity was associated with higher problematic social media, video game, and mobile phone use, compared to cisgender identification.

Conclusions: Transgender and gender-questioning adolescents spend a disproportionate amount of time engaging in screen-based activities and have more problematic use across social media, video game, and mobile phone platforms.

Introduction

Screen-based digital media is integral to the daily lives of adolescents in multifaceted ways [1] but problematic screen use (characterized by inability to control usage and detrimental consequences from excessive use including preoccupation, tolerance, relapse, withdrawal, and conflict) [2], [3], has been linked with harmful mental and physical health outcomes, such as depression, poor sleep, and cardiometabolic disease [4], [5]. Transgender and gender-questioning adolescents (i.e., adolescents who are questioning their gender identity) experience a higher prevalence of bullying (adjusted prevalence ratio [aPR] 1.88 and 1.62), suicide attempts (aPR 2.65 and 2.26), and binge drinking (aPR 1.80 and 1.50), respectively, compared to their cisgender peers [6], [7], [8], [9], [10]. Transgender and gender-questioning adolescents may engage in screen-based activities that are problematic and associated with negative health outcomes but also in a way that is different from their cisgender peers in order to form communities, explore health education about their gender identity, and seek refuge from isolating or unsafe environments [11].

One study found that sexual and gender minority (SGM) adolescents (e.g., lesbian, gay, bisexual, and transgender), aged 13–18 years old, spent an average of 5 h per day online, approximately 45 min more than non-SGM adolescents in 2010–2011 [12]. However, this study grouped SGM together as a single group, conflating the experiences of gender minorities (e.g., transgender, gender-questioning) with those of sexual minorites (e.g., lesbian, gay, bisexual), and the data are now over a decade old. In a nationally representative sample of adolescents aged 13–18 years old in the U.S., transgender adolescents had higher probabilities of problematic internet use than cisgender adolescents. However, this analysis did not measure modality-specific problematic screen use such as problematic social media, video game, or mobile phone use, which may further inform the function that media use plays in the lives of gender minority adolescents [13]. While this prior research provides important groundwork to understand screen time and problematic use in gender minority adolescents, gaps remain in understanding differences in screen time and specific modalities of problematic screen use in gender minority early adolescents.

Our study aims to address the gaps in the current literature by studying associations between transgender and gender-questioning identity and screen time across several modalities including recreational and problematic social media, video game, and mobile phone use in a large, national sample of early adolescents. We hypothesized that among early adolescents, transgender identification and questioning one’s gender identity would be positively associated with greater recreational screen time and problematic screen use compared to cisgender identification.

==

tl;dr: Gender-mania is an online social contagion.

No shit. That's why these "authentic selves" and "innate identities" tend to evaporate when kids are detoxed from the internet.

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[fixomnia-scribble]

I think - I think - I just cracked the last big issue in my stats data analysis for my presentation. If I'm right, my remaining workload just went way down and I will be able to get around to actual presentation prep very soon.

For those who speak Stats, I'm at the stage of cross-checking the validity of my two extracted Principal Components by way of multiple linear regression against other related variables. Why? Because we're not allowed to use the much faster Cronbach's Alpha tool. We're supposed to show we understand how our models work, not just report output numbers. That's easy enough, but my dataset stinks for this particular type of analysis (My beloved prof called it "hilariously awful"), and I have had to create some pretty wild dummy variables to test against.

If this sounds like gibberish, you're not wrong.

Have planned an Indian dinner and mindless movie night with friend, after presentation.

[tvareta]

Multiple Regression using R

Introduction:

Multiple regression is a branch of linear regression which can be used to analyse more than two variables. In multiple regression there is one response and more than one predictor variables whereas in linear regression where one response variable and one predictor variable. The predicator variables are the dependent variables and the response variable are the independent variables. Considering the equation for multiple regression,

Y=mx1+mx2+mx3=b

Where Y is the response variable

m1, m2, m3 are predictor variables

Let us discuss two problems regarding multiple regression

Analysis using R:

Multiple regression using R is one of the widely and often used method which is easy to use and handle.

DATA SET USED:

· https://github.com/grantgasser/Complete-Multiple-Regression

Using this dataset, we study of the relation between degree of brand liking (Y) and moisture content (X1) and sweetness (X2) of the product, the following results were obtained from the experiment based on a completely randomized design.

Some of the steps which we has to be followed are

1. Load and view the dataset

2. Identifying the data linearity in R

3. Plotting the graph

4. Implementation of Multiple Regression

5. Prediction and Interpretation

Brand Preference:

In a small-scale experimental study of the relation between degree of the brand liking (Y) and moisture content (XI) and sweetness (X2) of the product, the following results were obtained from the experiment based on a completely randomized design (data are coded:)

Analyzation of the data:

Scatter plot:

The diagnostic aids show that firstly, there are no outliers and the distribution for each variable is normal. Additionally, looking at the correlation matrix, Y and X1 have significant positive correlation, Y and X2 are positively correlated, but less so than Y and X1 and there’s no correlation between X1 and X2.

Correlation Matrix:

The correlation matrix of the variables is plotted to check the correlation between the variables.

Summary:

The value of multiple R- squared is 0.9521 and the adjusted R- squared value is 0.9447. When the variable X2, is added to X1 we get a p-value of about 2.01e-05. F- statistic variable is larger than 1. Y= 37.65 + 4.425X1 + 4.375X2 is the result of the regression model. Holding the other variables constant, increasing one unit of X1 results in a 4.425 rise in brand liking degree, while increasing one unit of X2 results in a 4.375 increase in brand liking degree. Because the P values for each variable are less than 0.05, both X1 and X2 are significant.

QQ plot:

In this QQ- plot the points plotted all fall in the same line which clearly determines that the residuals follow normal distribution. There are no outliers and errors .

Shapiro test:

Shapiro Wilk test is a statistic normality test for a random data set. It can be used to analyse if the data set is normally distributed. By analysing the values, we get,

Model validation:

Regression vs Residual Plot:

The above given plot is a residual plot which indicates a pattern in the residuals and the fitted plot. Although the distribution appears to be pretty normal, there are outliers on both sides of the median, with more outliers to the right.

Breusch-Pagan test:

This is test can be used to determine whether the heteroscedasticity is present in a regression analysis

Prediction and Confidence level:

newX = data. frame (X1=newX1, X2 = newX2)

#Confidence interval (95%)

predict (fit, newX, interval="confidence")

#Prediction Interval (95%)

predict (fit, newX, interval="prediction"

Output:

Interpretation:

From the above analysis, we get that the R- square is about 95% is very good and the results are accurate and the overall relationship is significant.