BehaviorBehavior (American English) or behaviour (British English) is the range of actions and mannerisms made by individuals, organisms, systems or artificial entities in some environment. These systems can include other systems or organisms as well as the inanimate physical environment. It is the computed response of the system or organism to various stimuli or inputs, whether internal or external, conscious or subconscious, overt or covert, and voluntary or involuntary.
Discrete choiceIn economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods (e.g. first-order conditions) can be used to determine the optimum amount chosen, and demand can be modeled empirically using regression analysis.
Adaptive behaviorAdaptive behavior is behavior that enables a person (usually used in the context of children) to cope in their environment with greatest success and least conflict with others. This is a term used in the areas of psychology and special education. Adaptive behavior relates to everyday skills or tasks that the "average" person is able to complete, similar to the term life skills. Nonconstructive or disruptive social or personal behaviors can sometimes be used to achieve a constructive outcome.
G-type main-sequence starA G-type main-sequence star (spectral type: G-V), also often, and imprecisely called a yellow dwarf, or G star, is a main-sequence star (luminosity class V) of spectral type G. Such a star has about 0.9 to 1.1 solar masses and an effective temperature between about 5,300 and 6,000 K. Like other main-sequence stars, a G-type main-sequence star converts the element hydrogen to helium in its core by means of nuclear fusion, but can also fuse helium when hydrogen runs out.
A-type main-sequence starAn A-type main-sequence star (A) or A dwarf star is a main-sequence (hydrogen burning) star of spectral type A and luminosity class (five). These stars have spectra defined by strong hydrogen Balmer absorption lines. They measure between 1.4 and 2.1 solar masses () and have surface temperatures between 7,600 and 10,000 K. Bright and nearby examples are Altair (A7), Sirius A (A1), and Vega (A0). A-type stars do not have convective zones and thus are not expected to harbor magnetic dynamos.
B-type main-sequence starA B-type main-sequence star (B V) is a main-sequence (hydrogen-burning) star of spectral type B and luminosity class V. These stars have from 2 to 16 times the mass of the Sun and surface temperatures between 10,000 and 30,000 K. B-type stars are extremely luminous and blue. Their spectra have strong neutral helium absorption lines, which are most prominent at the B2 subclass, and moderately strong hydrogen lines. Examples include Regulus and Algol A.
K-type main-sequence starA K-type main-sequence star, also referred to as a K-type dwarf red dwarf, or orange dwarf, is a main-sequence (hydrogen-burning) star of spectral type K and luminosity class V. These stars are intermediate in size between red M-type main-sequence stars ("red dwarfs") and yellow/white G-type main-sequence stars. They have masses between 0.6 and 0.9 times the mass of the Sun and surface temperatures between 3,900 and 5,300 K. These stars are of particular interest in the search for extraterrestrial life due to their stability and long lifespan.
F-type main-sequence starAn F-type main-sequence star (F V) is a main-sequence, hydrogen-fusing star of spectral type F and luminosity class V. These stars have from 1.0 to 1.4 times the mass of the Sun and surface temperatures between 6,000 and 7,600 K.Tables VII and VIII. This temperature range gives the F-type stars a whitish hue when observed by the atmosphere. Because a main-sequence star is referred to as a dwarf star, this class of star may also be termed a yellow-white dwarf (not to be confused with white dwarfs, remnant stars that are a possible final stage of stellar evolution).
Behavior modificationBehavior modification is an early approach that used respondent and operant conditioning to change behavior. Based on methodological behaviorism, overt behavior was modified with consequences, including positive and negative reinforcement contingencies to increase desirable behavior, or administering positive and negative punishment and/or extinction to reduce problematic behavior. It also used Flooding desensitization to combat phobias.
Main sequenceIn astronomy, the main sequence is a continuous and distinctive band of stars that appears on plots of stellar color versus brightness. These color-magnitude plots are known as Hertzsprung–Russell diagrams after their co-developers, Ejnar Hertzsprung and Henry Norris Russell. Stars on this band are known as main-sequence stars or dwarf stars. These are the most numerous true stars in the universe and include the Sun. After condensation and ignition of a star, it generates thermal energy in its dense core region through nuclear fusion of hydrogen into helium.
O-type main-sequence starAn O-type main-sequence star (O V) is a main-sequence (core hydrogen-burning) star of spectral type O and luminosity class V. These stars have between 15 and 90 times the mass of the Sun and surface temperatures between 30,000 and 50,000 K. They are between 40,000 and 1,000,000 times as luminous as the Sun. The "anchor" standards which define the MK classification grid for O-type main-sequence stars, i.e. those standards which have not changed since the early 20th century, are (O7 V) and (O9 V).
Applied behavior analysisApplied behavior analysis (ABA), also called behavioral engineering, is a psychological intervention that applies empirical approaches based upon the principles of respondent and operant conditioning to change behavior of social significance. It is the applied form of behavior analysis; the other two forms are radical behaviorism (or the philosophy of the science) and the experimental analysis of behavior (or basic experimental laboratory research).
Multinomial logistic regressionIn statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).
Maximum likelihood estimationIn statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.
Linear regressionIn statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Organizational behaviorOrganizational behavior or organisational behaviour (see spelling differences) is the: "study of human behavior in organizational settings, the interface between human behavior and the organization, and the organization itself". Organizational behavioral research can be categorized in at least three ways: individuals in organizations (micro-level) work groups (meso-level) how organizations behave (macro-level) Chester Barnard recognized that individuals behave differently when acting in their organizational role than when acting separately from the organization.
CorrelationIn statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.
Maximum spacing estimationIn statistics, maximum spacing estimation (MSE or MSP), or maximum product of spacing estimation (MPS), is a method for estimating the parameters of a univariate statistical model. The method requires maximization of the geometric mean of spacings in the data, which are the differences between the values of the cumulative distribution function at neighbouring data points.
Maximum a posteriori estimationIn Bayesian statistics, a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution. The MAP can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data. It is closely related to the method of maximum likelihood (ML) estimation, but employs an augmented optimization objective which incorporates a prior distribution (that quantifies the additional information available through prior knowledge of a related event) over the quantity one wants to estimate.
Likelihood functionIn statistical inference, the likelihood function quantifies the plausibility of parameter values characterizing a statistical model in light of observed data. Its most typical usage is to compare possible parameter values (under a fixed set of observations and a particular model), where higher values of likelihood are preferred because they correspond to more probable parameter values.
