#multinomial

[beanthebugboi]

I don't have a deadname. I have a chosen name that I generally prefer to use, and a birthname that I'm still pretty attached to- even though it's not my favorite, I still consider it one of my names.

HOWEVER.

When I email someone and I LITERALLY SIGN OFF WITH "Sincerely, [chosen name]", and their response starts with "Hi [birthname]", (because that name is still associated with my email) I want to scream.

[your-bigender-big-brother]

Hey, let's talk about names!

Do you use multiple names? Share some of your favorites in the replies!

Also, don't forget to check out @gender-buddies if you want to see gender labels turned into elemental critters. I'm drawing 120 in total (plus bonus ones) and I have 108 done so far. You'll love it! - Your Bigender Big Brother 💙💚

[aeon-of-trailblaze]

Ok i thought abt it more. And weirdly enough? I'm more fine with it when I'm using Sunny as a name. Which is weird huh. Like yeah those neos *are* sun themed but what is the logic behind it???

It still feels unfamiliar and i don't think I'm capable of doing it irl

Also i kinda have 1st person neos as well. But idk. As much as like it i feel kind ashamed yknow? I know i shouldn't be but my RSD is too strong....

[covid-safer-hotties]

Reference archived on our website (Daily updates! More than 1,500 open-access covid studies!)

Summary Background Research on long COVID in China is limited, particularly in terms of large-sample epidemiological data and the effects of recent SARS-CoV-2 sub-variants. China provides an ideal study environment owing to its large infection base, high vaccine coverage, and stringent pre-pandemic measures.

Methods This retrospective study used an online questionnaire to investigate SARS-CoV-2 infection status and long COVID symptoms among 74,075 Chinese residents over one year. The relationships between baseline characteristics, vaccination status, pathogenic infection, and long COVID were analyzed using multinomial logistic regression, and propensity matching.

Findings Analysis of 68,200 valid responses revealed that the most frequent long COVID symptoms include fatigue (30.53%), memory decline (27.93%), decreased exercise ability (18.29%), and brain fog (16.87%). These symptoms were less prevalent among those infected only once: fatigue (24.85%), memory decline (18.11%), and decreased exercise ability (12.52%), etc. Women were more likely to experience long COVID, with symptoms varying by age group, except for sleep disorders and muscle/joint pain, which were more common in older individuals. Northern China exhibits a higher prevalence of long COVID, potentially linked to temperature gradients. Risk factors included underlying diseases, alcohol consumption, smoking, and the severity of acute infection (OR > 1, FDR < 0.05). Reinfection was associated with milder symptoms but led to a higher incidence and severity of long COVID (OR > 1, FDR < 0.05). Vaccination, particularly multiple boosters, significantly reduced long-term symptoms by 30%–70% (OR < 1, FDR < 0.05). COVID-19 participants also self-reported more bacterial, influenza and mycoplasma infections, and 8%–10% of patients felt SARS-CoV-2-induced chronic diseases.

Interpretation This survey provides valuable insights into long COVID situation among Chinese residents, with 10%–30% (including repeated infection) reporting symptoms. Monitoring at-risk individuals based on identified risk factors is essential for public health efforts.

Funding This study was funded by the China Postdoctoral Science Foundation (2022M723344, 2023M743729), Guangdong Basic and Applied Basic Research Foundation (2023A1515110489), and the Bill & Melinda Gates Foundation (INV-027420).

[taohun]

when I ask a TA something I am NOT asking for your opinion! especially when you can’t tell the difference between a multinomial and hypergeometric distribution!

[olahdatasemarang]

ECM algorithm for the multinomial logit model Use emlogit With (In) R Software

ECM algorithm for the multinomial logit model Use emlogit With (In) R Software ristek.link/emlogit

[pipperoo]

i hate you multinomial logistic regression models!!!!! die!!!!

[traverse-or]

Introduction to Choice Modeling in Product Development

Knowing customer preferences is essential in today's cutthroat industry to create items that appeal to target consumers. Choice modeling is one of the best tools available to businesses like Traverse Or that want to innovate and develop customer-centric products. Businesses can optimize product features, price, and overall strategy by using this technique, which offers insightful information on consumer decision-making processes.

What is Choice Modeling?

A statistical method for examining how people choose when given a variety of options is called choice modeling. It makes the assumption that buyers assess products according to certain criteria, including cost, quality, or brand reputation, and select the one that optimizes their perceived usefulness. This approach identifies the fundamental elements that affect purchasing decisions, going beyond superficial preferences.

Key approaches in choice modeling include:

Conjoint analysis assesses the trade-offs that customers make between various product attributes.

Discrete Choice Experiments (DCEs): Model situations in which participants must select between rival goods or services.

Why Use Choice Modeling in Product Development?

Choice modeling transforms product development from guesswork into a data-driven process. It allows companies to:

Identify Key Attributes: Find out which characteristics customers value the most.

Optimize Product Design: Make arrangements that suit the tastes of the consumer.

Forecast Market Demand: Estimate the impact of changing features or prices on sales.

Reduce Risk: Before committing to full-scale production, test concepts.

To make sure their cars satisfy consumer wants, automakers may utilize choice modeling, for instance, to rank items like improved safety features or fuel efficiency.

How Does Choice Modeling Work?

The process of choice modeling typically involves the following steps:

1.Define Objectives:

Decide which good or service will be examined.

Decide which characteristics and levels (such as price range and feature variants) will be examined.

2.Design Experiments:

Create situations in which participants assess fictitious product alternatives.

Make sure all attribute combinations are effectively investigated by using experimental designs.

3.Collect Data:

Conduct tests or surveys with a representative sample of the intended audience.

In decision-making scenarios that are simulated, respondents are asked to select between possibilities.

4. Analyze Results:

Utilize statistical models, such as multinomial logistic regression, to determine each attribute's relative relevance.

Create forecasting tools, like market simulators, to predict consumer behavior in a range of scenarios.

Applications of Choice Modeling

1. Product Design and Innovation

By identifying the aspects that consumers find most appealing, choice modeling helps businesses create goods that cater to certain needs. For example:

It can disclose preferences for screen size, battery life, or brand reputation in consumer electronics.

 It can pinpoint flavor characteristics or packaging styles in food items that influence consumer choices.

2. Pricing Strategy

Businesses can identify the best pricing levels to increase revenue without offending customers by examining price sensitivity.

3. Market Segmentation

Based on preferences, choice modeling can identify discrete consumer groupings, enabling businesses to customize products for various demographics.

4. Concept Testing

Choice modeling can assess different concepts to determine which has the greatest market potential prior to launching a new product. The most promising combinations, for instance, were found by testing 2,304 barbecue sauce possibilities for a fraction of the usual expenditures.

5. Line Extensions and Portfolio Optimization

Businesses can use choice modeling to introduce new products while minimizing cannibalization of existing offerings and maximizing overall profitability.

Case Study: Automotive Innovation

Using decision modeling, an automaker aimed to create a ground-breaking vehicle concept. The procedure was as follows:

Identifying 16 potential features (e.g., autonomous driving capability, eco-friendly materials).

creating trials in which survey respondents are shown 60 different scenarios.

Latent-class segmentation and hierarchical Bayes models are used to forecast customer preferences.

The findings ultimately guided the company's product strategy by allowing them to anticipate sales volumes for various configurations and prioritize high-value features.

Benefits of Choice Modeling for Traverse Or

As a forward-thinking company, Traverse Or can leverage choice modeling to:

Create cutting-edge goods that are suited to consumer demands.

By concentrating on high-impact features, development expenses can be decreased.

Use predictive analytics to improve decision-making.

Traverse Or can maintain a competitive edge and provide solutions that genuinely connect with customers by including choice modeling into your framework for product development.

Conclusion

One effective tool for contemporary product development is choice modeling. Businesses like Traverse Or may use it to optimize designs, decipher customer preferences, and make well-informed strategic choices. In a market that is becoming more and more competitive, companies can meet and even beyond client expectations by implementing this strategy.

[nursingwriter]

¶ … Instability The Cost of Instability Access to health insurance has become a major issue in America and Elizabeth Legerski, in her article titled "The Cost of Instability: The Effects of Family, Work, and Welfare Change on Low-Income Women's Health Insurance Status" discusses the effect of being a low-income woman in relation to their access to health insurance. In her research Legerski used what she called a "secondary analysis of three waves of data from the Welfare, Children, and Families Project" A Three-City Study using a series of multinomial logistic regression models." (Legerski, 2012, p.644) This means she conducted no actual research herself but analyzed the data that was collected from another study to use for hers. The research study that Legerski used collected data from low-income families in Boston, Chicago, and San Antonio over a period of time. The data was collected through surveys given to the family's primary female caregiver in 1999, 2001, and 2005. The data collected from the original study was analyzed by Legerski to study access to health insurance by low-income women over a period of time. Most studies on access to health insurance do not take into account that a person's situation often changes over time affecting their access. Legerski studied the data already collected, analyzed it using a number of statistical tools, and made conclusion based on that analysis. Legerski's conclusions include that fact that access to health insurance is often predicated on stable employment, something that low-income women do not often possess. Because these women often have unstable employment and opportunities, primarily taking employment where and when it is available, their access to health insurance is limited. Added to the fact that these women, in their attempt to support their families, often make too much money to qualify for government assistance in the form of Medicare, these women are far too likely to slip through the cracks and have no real access to the health care system. A system that Legerski states "is particularly inadequate at addressing the complexity of women's lives…" (Legerski, 2012, p.652) As the study of the dynamics of a society and social interaction, when an entire subgroup of a society is denied adequate health care,, the field of Sociology begs the question of how interactions within that society can allow something like this to happen. Legerski's article attempts to use sociological analysis to answer that question and comes to the conclusion that the health care system currently in place is not able to adequately cover the health concerns of everyone in society. This allows her to draw the conclusion that the current system is desperately in need of reform in order to address the problems uncovered in her analysis. While not primary research, Legerski's article does include her secondary analysis of data collected from another research project. She then uses the data collected and undertook a sociological analysis, using statistical tools, which allows her to draw her own conclusions and publish them in a scholarly journal. As described, this is a scientific research study which just happens to use data already collected for another purpose and is quite different from non-scholarly sources such as magazines or newspapers. Non-scholar sources do not require that research or analysis be done, and be reviewed by other scientists, in order to be published; scholarly sources do. In order to be published in a scholarly source, like Sociological Forum, there must be some scientific work and new information to be presented to the scientific community. Even if the data used was previously collected for another purpose, as was the case with Legerski, the fact that is was used to draw new conclusions makes it worthy of being considered a scientific paper and not just a non-scholarly source like a magazine or newspaper article. References Legerski, Elizabeth. (Sept. 2012). "The Cost of Instability: The Effects of Family, Work, and Welfare Change on Low-Income Women's Health Insurance Status." Sociological Forum, 27 (3), 641-657. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1573-7861.2012.01339.x/abstract Read the full article

[jarviscodinghub]

Solved ECE368 Lab 1: Classification with Multinomial and Gaussian Models

1 Na¨ıve Bayes Classifier for Spam Filtering In the first part of the lab, we use a Na¨ıve Bayes Classifier to build a spam email filter based on whether and how many times each word in a fixed vocabulary occurs in the email. Suppose that we need to classify a set of N emails, and each email n is represented by {xn, yn}, n = 1, 2, . . . , N, where yn is the class label which takes the value yn…

[your-bigender-big-brother]

I think having multiple names is really cool! I wish I could settle on a few names to use regularly, but there are too many names I really like and I'm always afraid of confusing people!

But I do use multiple names wherever I can: One name for this blog, one for my personal side blog, and one in person. Maybe someday I'll take on a second name to use in person based on how my gender might feel that day. Who knows!

Keep using multiple names. Keep hoarding names. Names are really useful and personal, and it always feels good when we get to hear our chosen names being used. - Your Bigender Big Brother 💙💚

[literaturereviewhelp]

Health Lifestyle Part A: Quantitative Article by Burdette, Needham, Hill, and Taylor Background Lifestyles have a correlation effect on our health, and we must define health not from a singular perspective but from a society base to collect much data, which shows health trends and behavior in the community. The researchers recognize that little research has been done to determine the lifestyle behavior on our overall health care and that the impact can be huge if no quantitative and qualitative research is done. The researchers have set out a few types of research carried out on related topics. They identify the studies done on an association of holding multiple social roles and health outcomes. However, the authors have objectively laid out the problem statement, which is, how acquiring multiple roles in early adulthood influence health behavior. Literature review The researchers have used several past projects carried out by different others. The topics in the literature review are problems related to the topic the researchers were handling. Some questions have touched on population and general lifestyle behaviors and their effects on health. “Health and lifestyles," and "Human Health Development" are the key topics the researchers discussed. Other topics include: "Diabetes Prevention Program," "Diabetes Prevention," “Health Behaviors in Adolescents” and “Lifestyle Health Behavior in Adults." The topics had limitations and had not addressed the particular concern that the statement was raising. Methodology A quantitative approach was the preferred method for the authors. Data obtained from Add Health, an institution that primarily collects adolescence data from schools, families and other learning institutions. Sampled data was already available, and quantitative analysis was used to define a critical process of calculation. The measurement was also used as the subtype to calculate the variables. Self-rating was part of the health analysis used to obtain data. Data analysis Data were evaluated using a Latent Class Analysis (LCA), this involved model latent healthy lifestyle from observed health behavior indicators. LCA is similar to other reduction data analysis techniques. LCA uses the variable with a multinomial distribution to establish clusters of individuals based on observed indicators Secondly, predicting healthy lifestyle based on self-health measuring, and role occupancy during the transition to early adulthood was another suitable method (Burdette, Needham, Hill, Taylor, 2017).Latent class analysis was also used as both a dependent and independent variable in the data analyses. Conclusion The results gave a significant approach to lifestyle health. The researchers suggested the importance of modeling overall health instead of focusing on an individual health behavior. Secondly, the result demonstrated how holding many social lifestyles influence health behavior. The conclusion makes sense because the results explain a correlation between essential roles and health behavior, which is already shown in primary health care (Burdette et al., 2017). The researchers have also concluded that adolescence lifestyle has a significant influence on self-rated health. Researcher’s Conclusion Analysis The investigator's conclusion emphasizes on the significance of concentrating on overall healthy lifestyles rather than individual approach to health behavior. The literature review showed that there was little research carried out on the purpose statement but data collected had demonstrated that overall approach to social lifestyle gave particular health behavior (Burdette et al., 2017). The quantitative method was the best approach to use for this type of research because it gives both numerical and descriptive data as supported by the results. Protection and Consideration The research does not plainly admit to getting parental consent from adolescence bracket who are below 18 years, and this is a breach of ethical standards in research and research formulation. Even though the study was voluntary for students, were they informed? Ethical standards, especially when getting data from children below eighteen years is a serious violation. Strengths and Limitations The authors used a broad sample base, which enhances the accuracy of the results. Strength in the research is the sample size. In one year, a record of 15,000 young adults, 18000 parents and 143 school officials were interviewed (Burdette et al., 2017). The large sample size represents a significant data with variables that can give an overall picture of the lifestyle behavior on the health of young adults. Conversely, a young group may not have the right perspective about their health and may give false information. For example, the interviews conducted at home in the presence of their parents may have a lot of errors; telling the interviewer that you drink and your parents are sitting across is the unlikely occurrence. Read the full article

[programmingandengineering]

ECE368 Lab 1: Classification with Multinomial and Gaussian Models

Naive Bayes Classifier for Spam Filtering In the rst part of the lab, we use a Na ve Bayes Classi er to build a spam email lter based on whether and how many times each word in a xed vocabulary occurs in the email. Suppose that we need to classify set of N emails, and each email n is represented by fxn; yng; n = 1; 2; : : : ; N, where yn is the class label which takes the value yn = ( 1 if…

[myprogrammingsolver]

ECE368 Lab 1: Classification with Multinomial and Gaussian Models

Naive Bayes Classifier for Spam Filtering In the rst part of the lab, we use a Na ve Bayes Classi er to build a spam email lter based on whether and how many times each word in a xed vocabulary occurs in the email. Suppose that we need to classify set of N emails, and each email n is represented by fxn; yng; n = 1; 2; : : : ; N, where yn is the class label which takes the value yn = ( 1 if…

[inclusiveuniversity]

Chronic Illness in College Students: Assessing Exercise Behaviors, Motivation, Barriers, and Psychological Factors

Research on the intersectionality of exercise, motivation, barriers, functional disability, psychological factors, and CI in undergraduate college students is limited. The aim of this dissertation was to investigate relationships between exercise behaviors, exercise motivation, barriers to exercise, functional disability, and psychological factors (i.e., anxiety, depression) amongst healthy undergraduate students and those with chronic illnesses (CI). Exercise behaviors, motivation, and barriers were compared across health status (CI vs. healthy) and the predictive capacities of functional disability and psychological factors were evaluated. Undergraduate students (N=200) completed online surveys (Qualtrics). Statistical analyses performed included Hotellings T2, multiple linear regression, and multinomial logistic regression. Findings displayed no differences between health status groups on motivation, but the CI group reported significantly more barriers. Functional disability and depression significantly positively predicted barriers to exercise for both groups. Functional disability significantly inversely predicted physical activity (PA) for students with CIs and significantly positively predicted PA for healthy students. Depression was found to significantly inversely predict PA for healthy students. Anxiety displayed no effect on PA or barriers for either the healthy student or those with CIs. Lastly, students reporting higher functional disability or depression displayed statistically increased odds of motivation from external regulation as opposed to internal regulation. Universities could use this research to implement programs aimed at increasing PA through teaching providers Motivational Interviewing (MI) techniques. Practitioners could use Cognitive Behavioral Therapy to benefit students in changing their perceptions about barriers to exercise and functional disability.