Model : A model is a representation or simulation of a real-world system or concept. It is often used to help understand complex phenomena and to make predictions about how the system will behave in different situations. There are many different types of models, but two common examples are mathematical models and conceptual models. A […]

Mode : Mode is a statistical measure that represents the most frequent value in a data set. It is often used to summarize a set of observations and provide a quick and easy way to understand the distribution of the data. For example, consider a data set of exam scores for a class of students. […]

MLOps : MLOps, or Machine Learning Operations, is the practice of integrating machine learning models into the software development lifecycle. It involves a collaboration between data scientists, software engineers, and IT operations teams to ensure that machine learning models are efficiently deployed, monitored, and maintained in a production environment. One example of MLOps in action […]

ML-as-a-Service (MLaaS) : MLaaS, or Machine Learning as a Service, is a term used to describe the practice of providing machine learning capabilities through the cloud. This allows organizations to leverage the power of machine learning without the need for expensive hardware and specialized expertise. One example of MLaaS is Amazon Web Services (AWS) which […]

MIS : Management Information Systems, or MIS, is a term used to describe the integration of technology, people, and business processes to manage and analyze data in order to support decision-making and improve organizational efficiency. In today’s digital age, MIS plays a critical role in supporting the operations of businesses, governments, and other organizations. One […]

Mixture transition distribution model : A mixture transition distribution model is a type of statistical model that represents the distribution of a random variable as a mixture of other distributions. In other words, it is a probabilistic model that assumes that the underlying distribution of the random variable is composed of multiple sub-distributions, each with […]

Mixed-effects logistic regression : Mixed-effects logistic regression is a type of regression analysis that allows for the examination of both fixed and random effects within a single model. This type of analysis is useful when studying data that has a hierarchical or nested structure, such as when multiple observations are made within each subject or […]

Misspecification : Misspecification refers to the incorrect specification or construction of a model. In other words, it refers to the situation when the model used to analyze a particular phenomenon or data does not accurately capture the underlying relationships and patterns. This can lead to inaccurate or misleading results and conclusions. One example of misspecification […]

Missing values : Missing values, also known as missing data or missing observations, refer to the lack of information or data for a specific variable in a dataset. This can occur for various reasons, such as when a survey respondent does not answer a question, when a measurement is not taken, or when data is […]

Mis-interpretation of P-values : P-values are a common statistical measure used to determine the likelihood of a given result occurring by chance. A low P-value indicates a strong likelihood that the result is not due to chance, while a high P-value suggests that the result could have occurred by chance. However, there is a tendency […]