The Most Popular GLMM: Revealing the Top Choices

Choose the GLMM you think is the most popular!

Author: Gregor Krambs
Updated on Feb 29, 2024 06:08
Welcome to StrawPoll's latest and most exciting ranking event, where we delve into the phenomenal world of GLMMs! As an avid viewer, you know that Gacha Life Mini Movies have taken the internet by storm, captivating audiences with their unique animations and gripping storylines. Now, it's time for you to make your voice heard by voting for the most popular GLMM of all time! Will it be a heartwarming romance, a thrilling mystery, or a gut-busting comedy? The power is in your hands – and if you feel like we missed a gem, don't worry! You can suggest your favorite GLMM to be included in the ranking. So, what are you waiting for? Get ready to explore the best of the best in Gacha Life Mini Movies, and let the voting begin!

What Is the Most Popular GLMM?

  1. 1
    Logistic regression
    Canley · CC BY-SA 4.0
    It is a widely used GLMM for binary outcomes in medical, social, and behavioral sciences.
    Logistic regression is a statistical model that is used to analyze the relationship between a binary dependent variable and one or more independent variables. It is a popular generalized linear mixed model (GLMM) that is commonly used in various fields, such as social sciences, medicine, and business analytics.
    • Type: Generalized linear mixed model
    • Dependent Variable: Binary
    • Independent Variables: One or more
    • Distribution: Logistic
    • Link Function: Logit
  2. 2

    Poisson regression

    George Udny Yule
    It is commonly used in count data analysis, such as in epidemiology or ecology.
    Poisson regression is a type of generalized linear mixed model (GLMM) commonly used for analyzing count data. It estimates the relationship between a dependent variable representing counts and one or more independent variables. Poisson regression assumes that the dependent variable follows a Poisson distribution and models the expected value of the counts as a function of the independent variables using a logarithmic link function.
    • Distribution: Poisson
    • Link Function: Logarithmic
    • Dependent Variable: Count data
    • Assumption: Independence of observations
    • Parameter Estimation: Maximum Likelihood Estimation (MLE)
  3. 3
    It is used when the count data has over-dispersion, which means that the variance is greater than the mean.
    Negative binomial regression is a statistical method used for modeling count data. It is an extension of the Poisson regression model that relaxes the assumption of equidispersion and allows for overdispersion. The negative binomial regression model assumes that the number of events follows a negative binomial distribution rather than a Poisson distribution.
    • Distribution: Negative Binomial
    • Dependent Variable Type: Count
    • Assumption: Overdispersion
    • Link Function: Logarithmic
    • Mean-Variance Relationship: Multiplicative
  4. 4

    Mixed-effects regression

    Charles E. McCulloch
    It is used to model data with both fixed and random effects, such as longitudinal or clustered data.
    Mixed-effects regression, also known as multilevel or hierarchical regression, is a statistical method used in analyzing data that has a hierarchical or nested structure. It is an extension of linear regression that accounts for the correlation or clustering within the data. It allows for modeling both fixed effects (predictors) and random effects (variation between groups or subjects) simultaneously.
    • Flexibility: Can handle both continuous and categorical predictors.
    • Hierarchical Structure: Allows for modeling data with a hierarchical structure, such as clustered or repeated measures data.
    • Random Effects: Can model random effects to account for variation between groups or subjects.
    • Variance Partitioning: Provides estimates of the variance components, quantifying the amount of variability explained by fixed and random effects.
    • Group-level Effects: Can estimate group-level effects, allowing for comparisons of groups within the same model.
  5. 5
    It is a semi-parametric method to analyze correlated data, such as repeated measures, cluster samples, or panel data.
    Generalized estimating equations (GEE) is a statistical method used for analyzing longitudinal or clustered data. It extends the generalized linear model (GLM) to handle correlated or clustered data by accounting for the within-group correlation structure. GEE provides population-averaged estimates by assuming a working correlation structure and estimating the regression coefficients using an iterative algorithm.
    • Type: Statistical method
    • Purpose: Analyze longitudinal or clustered data
    • Extension: Generalized linear model (GLM)
    • Correlation structure: Within-group correlation
    • Estimation: Iterative algorithm
  6. 6
    It is a non-parametric method to model data with a continuous response variable, such as in geostatistics, machine learning, or Bayesian modeling.
    Gaussian process regression is a popular probabilistic regression model used in machine learning and statistics. It is based on the concept of Gaussian processes, which are a collection of random variables that jointly follow a Gaussian distribution. This model allows for non-linear regression by using a set of training data to learn a function that can predict the output values for unseen inputs.
    • Flexibility: Gaussian process regression can model non-linear relationships between variables and can handle different input spaces.
    • Uncertainty Estimation: It provides a measure of uncertainty for its predictions, which is useful in decision-making or finding confidence intervals.
    • Suitability for Small Data: It can handle small datasets effectively due to its Bayesian framework and ability to capture complex patterns.
    • Non-parametric: It does not make any strong assumptions about the underlying distribution of the data, making it a flexible modeling tool.
    • Scalability: Efficient algorithms exist for Gaussian process regression, allowing it to handle moderate-sized datasets.
  7. 7

    Zero-inflated regression

    Joseph C. Hilbe
    It is used to model data with excessive zeros, such as in economic, ecological, or marketing research.
    Zero-inflated regression is a statistical method used to model data with excessive zero counts. It is commonly employed in situations where the data exhibit two types of zeros: structural zeros and sampling zeros. Structural zeros arise from the inherent characteristics of the population, while sampling zeros occur due to random variability. The model estimates two separate processes: one to model the count of zeros and another to model the non-zero counts.
    • Type: Regression
    • Distribution: Mixed distribution (a combination of zero-inflated and non-zero distribution)
    • Purpose: To account for excessive zero counts in the data
    • Applications: Healthcare research, economic modeling, environmental sciences
    • Software Packages: R: 'pscl', 'glmmTMB', Stata: 'ziptool', SAS: 'HPCOUNT', Python: 'statsmodels'
  8. 8
    It is used to model data with ordinal response variables, such as in psychology, education, or marketing research.
    Ordinal regression, also known as ordered logistic regression or proportional odds regression, is a statistical model used to analyze and predict the relationship between an ordinal dependent variable and one or more independent variables. It is an extension of binary logistic regression that handles ordered categorical outcomes, where the categories have a natural ordering but do not have equal spacing between them.
    • Model Type: Generalized Linear Mixed Model (GLMM)
    • Dependent Variable: Ordinal
    • Independent Variable(s): One or more
    • Assumptions: Proportional odds assumption, independence of observations
    • Link Function: Logit
  9. 9

    Tobit regression

    James Tobin
    It is used to model data with censored or truncated response variables, such as in economics, finance, or health research.
    Tobit regression is a statistical analysis method used for examining the relationship between a censored dependent variable and one or more independent variables. It was developed by James Tobin in 1958.
    • Dependent variable type: Censored, continuous variable
    • Assumptions: Normality, linearity, homoscedasticity
    • Censoring: Right-censoring (values below a certain threshold)
    • Estimation method: Maximum likelihood estimation
    • Inference: Hypothesis testing, confidence intervals
  10. 10

    Multilevel regression

    Douglas Bates and Donald Watts
    It is used to model data with hierarchical structures, such as in education, sociology, or geography research.
    Multilevel regression, also known as hierarchical linear modeling or mixed-effects modeling, is a statistical technique used to analyze data with nested or hierarchical structures. It allows for the modeling of individual-level and group-level effects simultaneously, accounting for the dependencies between observations within groups.
    • 1: Allows modeling of nested or hierarchical data structures
    • 2: Accounts for dependencies between observations within groups
    • 3: Estimates fixed effects at both individual and group levels
    • 4: Incorporates random effects to capture variations among groups
    • 5: Can handle unbalanced data structure

Missing your favorite GLMM?


Ranking factors for popular GLMM

  1. View Count
    The number of views a GLMM has garnered on video-sharing platforms, such as YouTube. A higher view count indicates greater popularity.
  2. Likes and Dislikes
    The number of likes and dislikes a GLMM has received. A higher like-to-dislike ratio indicates a more favorable audience reception.
  3. Engagement
    The level of audience interaction, such as comments and shares, with the GLMM. Higher engagement indicates more active discussions and greater interest in the content.
  4. Creator Popularity
    The popularity and reputation of the Gacha Life content creator. If a creator has a large and loyal following, their GLMMs are likely to be more popular.
  5. Originality and Creativity
    The uniqueness and creativity of the storyline, characters, and overall quality of the GLMM. Factors to consider here include plot twists, character development, and execution of themes.
  6. Production Quality
    The quality of the GLMM in terms of visuals, audio, editing, and overall presentation.
  7. Recommendations
    The number of times a GLMM is recommended by others, both online and offline. Word-of-mouth referrals and mentions in other videos, articles, or social media posts can significantly contribute to a GLMM's popularity.
  8. Awards and Recognitions
    Any awards or recognitions received by the GLMM or its creator can indicate the popularity and quality of the content.
  9. Genre
    Certain genres or themes may be more popular and appealing to a wider audience, contributing to a GLMM's overall popularity.
  10. Upload Date
    The recency of the GLMM's upload can impact its popularity. A video that has been up for a longer time may have more views and engagement, whereas newer videos may not have had the chance to gain as much attention yet. Keep in mind, however, that older videos may be less relevant or may not reflect current trends or interests.

About this ranking

This is a community-based ranking of the most popular GLMM. We do our best to provide fair voting, but it is not intended to be exhaustive. So if you notice something or GLMM is missing, feel free to help improve the ranking!


  • 170 votes
  • 10 ranked items

Voting Rules

A participant may cast an up or down vote for each GLMM once every 24 hours. The rank of each GLMM is then calculated from the weighted sum of all up and down votes.

More information on most popular glmm

GLMM or "Gacha Life Mini Movie" has become a popular trend among the Gacha community. It is a form of animation that uses the Gacha Life app to create short movies featuring Gacha characters. These mini movies often tell a story, with a plot, characters, and dialogue. GLMMs have gained a massive following on social media platforms such as YouTube, TikTok, and Instagram, with millions of views and likes. The popularity of GLMMs has led to a wide range of themes, from romance and drama to action and horror, making it a versatile form of entertainment. With so many GLMMs out there, it's no wonder that people are curious about which ones are the most popular. Let's take a closer look at some of the most popular GLMMs and why they have captured the attention of so many Gacha fans.

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