Component analysis in statistics refers to techniques used to understand the underlying structure of data by decomposing it into its constituent parts or components. These techniques are often used for data reduction, exploration, and visualization. The most common forms of component analysis include: 1. **Principal Component Analysis (PCA)**: PCA is a technique that transforms a dataset into a set of linearly uncorrelated components, known as principal components.

Explained variation refers to the portion of the total variation in a dataset that can be attributed to a specific model or statistical relationship among variables. In other words, it measures how much of the variability in a dependent variable can be explained by one or more independent variables. In the context of regression analysis, for example, explained variation can be quantified through the coefficient of determination, commonly denoted as \( R^2 \).

Haseman–Elston regression is a statistical method used in genetic epidemiology to analyze the relationship between genetic traits and various phenotypic outcomes. Specifically, this approach is often employed to assess the genetic correlation between relatives, such as siblings, in relation to a particular trait or disorder.

Optimal design refers to the process of determining the most effective way to achieve specific objectives within a given set of constraints. This concept is widely used in various fields, including engineering, statistics, economics, and research design. The core idea is to find a design that maximizes or minimizes a particular function—often referred to as the objective function—while adhering to the limitations imposed by resources, conditions, or requirements.

Lasso, which stands for "Least Absolute Shrinkage and Selection Operator," is a statistical method used primarily in regression analysis. It is particularly useful for feature selection and regularization when dealing with a large number of predictors in a regression model. Here's an overview of its key characteristics: 1. **Regularization**: Lasso adds a penalty term to the ordinary least squares (OLS) regression cost function. This penalty is proportional to the absolute values of the coefficients of the predictors.

Moderated mediation is a statistical concept that examines the interplay between mediation and moderation in a model. In a mediation model, a variable (the mediator) explains the relationship between an independent variable (IV) and a dependent variable (DV). In contrast, moderation refers to the idea that the effect of one variable on another changes depending on the level of a third variable (the moderator).

Nonlinear regression is a type of regression analysis in which the relationship between the independent variable(s) and the dependent variable is modeled as a nonlinear function. Unlike linear regression, which assumes a straight-line relationship (a linear equation) between the variables, nonlinear regression allows for more complex relationships, accommodating curves and other non-linear shapes.

Pyrrho's lemma is a concept from probability theory, specifically related to the properties of random variables. It is named after the ancient Greek philosopher Pyrrho, who is known for his contributions to skepticism and the idea of certain knowledge. However, in the context of probability, it is more often related to the study of convergence and the behavior of random sequences.

Smearing retransformation is a statistical method often used in the context of regression analysis, particularly when dealing with models that involve transformation of the dependent variable. The method addresses the issue of bias that can arise when transforming data, especially when the outcome is log-transformed or otherwise modified to meet model assumptions.

Unit-weighted regression is a type of regression analysis where each predictor variable (independent variable) is assigned the same weight (usually a weight of one) in the model, regardless of the individual significance or scale of the predictors. This approach simplifies the modeling process by treating each predictor equally when predicting the dependent variable (the outcome).

Virtual sensing refers to the process of estimating or predicting certain physical quantities or parameters without direct measurement, often using mathematical models, algorithms, or data from other sensors. Instead of using dedicated sensors for every parameter, virtual sensors leverage existing data (possibly from multiple sources) and apply algorithms—like machine learning, statistical methods, or physical models—to calculate the values of interest. **Key aspects of virtual sensing include:** 1.

Civis Analytics is a data science and analytics firm that focuses on helping organizations, particularly those in the public and nonprofit sectors, leverage data to drive decision-making and improve outcomes. Founded in 2013 by several former members of the Obama campaign's data team, Civis Analytics provides services that include data strategy, analytics, and consulting, as well as a software platform that allows clients to analyze data more effectively.

Nuclear weapons delivery refers to the methods and systems used to transport and deploy nuclear weapons to their intended targets. The effectiveness of a nuclear weapon is not only dependent on the weapon itself but also largely on how it is delivered. Various delivery systems are designed to ensure that nuclear warheads can reach their targets accurately and efficiently while surviving any potential countermeasures.

"Numberblocks" is a children's educational television series created by Blue Zoo Productions for the BBC. The show is designed to teach young children about numbers and basic math concepts in a fun and engaging way. It features colorful, animated characters known as the Numberblocks, each representing a different number. The characters are personified numbers, with each Numberblock having unique traits, personalities, and abilities that reflect their numerical value.

The TV series "Numbers" (often stylized as "Numb3rs") is a crime drama that aired from 2005 to 2010, focusing on a mathematics professor who helps the FBI solve crimes using mathematical principles.

Numerical artifacts refer to errors or distortions in numerical data or results that arise due to various factors in computational processes. These artifacts can occur in simulations, numerical methods, data collection, or processing, and can negatively impact the accuracy and reliability of analyses and conclusions. Some common sources of numerical artifacts include: 1. **Rounding Errors**: When numbers are rounded to a certain number of significant digits, this can introduce small inaccuracies, especially in iterative calculations.

An oblique lattice refers to a specific type of two-dimensional lattice structure in crystallography and solid-state physics. In geometry, a lattice is a regular arrangement of points in space, and in the context of crystallography, it often describes the arrangement of atoms in a crystal. An oblique lattice is characterized by two non-orthogonal basis vectors that define a parallelogram in a two-dimensional space.

An Ocean Data Acquisition System (ODAS) refers to a set of tools and technologies used to collect, process, and store various types of data related to the ocean and marine environments. These systems are integral to marine research, environmental monitoring, and various commercial applications, such as fishing, shipping, and oil and gas exploration.

Octahedral molecular geometry is a three-dimensional shape that occurs when a central atom is surrounded symmetrically by six other atoms or groups of atoms (ligands). In this arrangement, the central atom is positioned at the center of an octahedron, and the six surrounding atoms occupy the corners of this geometric shape. Key characteristics of octahedral molecular geometry include: 1. **Bond Angles**: The bond angles between the atom pairs are all 90 degrees, providing a symmetrical arrangement.

Octavius Wigram is primarily known as a historical figure associated with the British naval and marine industries in the 19th century. He was particularly noted for his involvement in shipbuilding and his contributions to the design of naval vessels. His legacy includes influence on maritime engineering and advancements in naval architecture during that era.