Joseph Dalton Hooker (1817–1911) was a prominent British botanist, explorer, and one of the founders of modern plant geography. He was a key figure in the study of plant taxonomy and biogeography, and he was the son of William Jackson Hooker, a notable botanist and director of the Royal Botanic Gardens, Kew.

Marcellin Berthelot (1827–1907) was a prominent French chemist and politician known for his significant contributions to the fields of organic chemistry and chemical thermodynamics. He is particularly recognized for his work on the synthesis of organic compounds and the study of thermochemical processes.

Oswald Avery was a Canadian microbiologist and a key figure in the field of genetics. He is best known for his role in the discovery that DNA is the substance that causes bacterial transformation. This groundbreaking research was conducted in the early 1940s, particularly through his work with pneumococcus bacteria.

Regression variable selection is the process of identifying and selecting the most relevant predictor variables (or independent variables) to be included in a regression model. The goal is to improve the model's performance by eliminating unnecessary noise introduced by irrelevant or redundant variables, enhancing interpretability, and potentially improving model accuracy. Here are some key aspects of regression variable selection: 1. **Purpose**: The main purposes of variable selection include reducing model complexity, avoiding overfitting, and simplifying the interpretation of the model.

Stephen Hales (1677-1761) was an English minister, scientist, and notable early figure in the field of botany and physiology. He is best known for his pioneering work in plant physiology, particularly his studies on plant transpiration and their ability to absorb water and nutrients from the soil. Hales conducted various experiments that laid the groundwork for understanding fluid movement in plants.

Tomas Lindahl is a prominent Swedish chemist known for his groundbreaking work in the field of DNA repair and molecular biology. He was awarded the Nobel Prize in Chemistry in 2015, sharing it with Paul L. Modrich and Aziz Sancar, for their contributions to our understanding of how cells repair damaged DNA, a critical process that helps maintain genetic stability and prevents diseases such as cancer.

William Lewis may refer to several individuals, but without specific context, it's challenging to identify which scientist you are referring to. One prominent William Lewis is known for his work in evolutionary biology and behavioral ecology. He has contributed to understanding the evolution of animal behavior, particularly in the fields of ornithology and ecology.

Course-of-values recursion is a concept in computer science and programming languages, particularly in relation to the design of recursive functions. It refers to a specific style of recursion where the function computes values of subproblems first and stores them in some form of intermediate structure (such as a list or an array) before making use of these computed values to produce the final result. In traditional recursion, a function may call itself multiple times for subproblems, recalculating values each time the subproblem appears.

Walther recursion is a method used in functional programming and formal language theory to define functions that can be computed via recursive calls. It builds on the concept of general recursion while emphasizing the structure of recursive definitions. The central idea of Walther recursion is to express a function in terms of a "primitive recursion" along with an additional layer that allows for the use of previously computed values in the recursive process.

Many-one reduction, also known as **mapping reduction**, is a concept in computational complexity theory used to compare the difficulty of decision problems. It involves transforming instances of one decision problem into instances of another decision problem in such a way that the answer to the original problem can be easily derived from the answer to the transformed problem.

Cross-sectional regression is a statistical technique used to analyze data collected at a single point in time across various subjects, such as individuals, companies, or countries. This method involves estimating the relationships between one or more independent variables (predictors or explanatory variables) and a dependent variable (the outcome or response variable) by fitting a regression model.

Meta-regression is a statistical technique used in meta-analysis to examine the relationship between study-level characteristics (often referred to as moderators) and the effect sizes reported in different studies. Its primary purpose is to explore how variations in study design, sample characteristics, or measurement methods may influence the outcomes of interest. In essence, meta-regression extends traditional meta-analysis by allowing researchers to assess how certain factors (e.g., age of participants, length of intervention, type of treatment, etc.

The Principle of Marginality, often associated with economics and decision-making theories, suggests that in assessing the impact or utility of a decision, one should focus on the effects of incremental changes rather than the total or average effects. This principle emphasizes that when making decisions, individuals or organizations should consider the marginal benefits and marginal costs—the additional benefits gained from an action compared to the additional costs incurred.

The Heckman correction, also known as the Heckman two-step procedure, is a statistical method used to correct for selection bias in econometric models. Selection bias occurs when the sample collected for analysis is not randomly selected from the population, which can lead to biased parameter estimates if ignored.

Homoscedasticity and heteroscedasticity are terms used in statistics and regression analysis to describe the variability of the error terms (or residuals) in a model. Understanding these concepts is important for validating the assumptions of linear regression and ensuring the reliability of the model's results.

Interaction cost refers to the resources expended—such as time, effort, or financial expenditure—when individuals or organizations engage in communications or interactions with one another. This concept is commonly discussed in various fields, including economics, business, and information technology. Key aspects of interaction cost include: 1. **Time Costs**: The amount of time spent in communication, whether face-to-face, via email, or other forms.

Multinomial probit is a statistical model used to analyze dependent variables that are categorical and have more than two outcomes. It is particularly useful when the choice or outcome is not ordinal (i.e., there's no inherent order among the categories) but is rather nominal. ### Key Features of Multinomial Probit: 1. **Categorical Dependent Variable**: The model is designed for dependent variables that can take on multiple categories.

SIMNET (Simulated Network) is a distributed simulation environment originally developed for training military personnel, particularly for tank warfare. It allows different users to connect over a network and participate in realistic combat scenarios using simulators. The goal of SIMNET is to create a virtual battlefield where participants can operate vehicles, command units, and interact with others in real-time.

A polygenic score (also known as a polygenic risk score or PRS) is a numerical value that reflects an individual's genetic predisposition to a particular trait or disease. It is calculated based on the cumulative effects of multiple genetic variants, each of which may contribute a small amount to the overall risk or expression of that trait.

Strongly regular graphs are a special class of graphs characterized by their regularity and specific connection properties between vertices. A graph \( G \) is called strongly regular with parameters \( (n, k, \lambda, \mu) \) if it satisfies the following conditions: 1. **Regularity**: The graph has \( n \) vertices, and each vertex has degree \( k \) (i.e., it is \( k \)-regular).