Statistical Glossary Local Independence: The local independence postulate plays a central role in latent variable models . Local independence means that all the manifest variable s are independent random variables if the latent variable s are controlled (fixed). Technically, the local independence may be described by formula P(y1, ......
View Full DescriptionManifest Variable: In latent variable models , a manifest variable (or indicator) is an observable variable - i.e. a variable that can be measured directly. A manifest variable can be continuous or categorical. The opposite concept is the latent variable . See also latent variable models Browse Other Glossary Entries
View Full DescriptionMarginal Density: If X and Y are continuous random variables, and f(x,y ) is the joint density of X and Y, then the marginal density of X, g(x), is given by Browse Other Glossary Entries
View Full DescriptionMarginal Distribution: If X and Y are discrete random variables and f(x,y) is their joint probability distribution, the marginal distribution of X, g(x) is given by Browse Other Glossary Entries
View Full DescriptionMeta-analysis: Meta-analysis takes the results of two or more studies of the same research question and combines them into a single analysis. The purpose of meta-analysis is to gain greater accuracy and statistical power by taking advantage of the large sample size resulting from the cumulation of results over multiple...
View Full DescriptionMinimax Decision Rule: A minimax decision rule has the smallest possible maximum risk. All other decision rules will have a higher maximum risk. Browse Other Glossary Entries
View Full DescriptionMulticollinearity: In regression analysis , multicollinearity refers to a situation of collinearity of independent variables, often involving more than two independent variables, or more than one pair of collinear variables. Multicollinearity means redundancy in the set of variables. This can render ineffective the numerical methods used to solve regression regression...
View Full DescriptionMultivariate: Multivariate analysis involves more than one variable of interest. Browse Other Glossary Entries
View Full DescriptionNeural Network: A neural network (NN) is a network of many simple processors ("units"), each possibly having a small amount of local memory. The units are connected by communication channels ("connections") which usually carry numeric (as opposed to symbolic) data, encoded by any of various means. The units operate only...
View Full DescriptionNominal Scale: A nominal scale is really a list of categories to which objects can be classified. For example, people who receive a mail order offer might be classified as "no response," "purchase and pay," "purchase but return the product," and "purchase and neither pay nor return." The data so...
View Full DescriptionNormality: Normality is a property of a random variable that is distributed according to the normal distribution . Normality plays a central role in both theoretical and practical statistics: a great number of theoretical statistical methods rest on the assumption that the data, or test statistics derived from a sample...
View Full DescriptionOrdinal Scale: An ordinal scale is a measurement scale that assigns values to objects based on their ranking with respect to one another. For example, a doctor might use a scale of 0-10 to indicate degree of improvement in some condition, from 0 (no improvement) to 10 (disappearance of the...
View Full DescriptionOutlier: Sometimes a set of data will have one or more items with unusually large or unusually small values. Such extreme values are called outliers. Outliers often arise from some mistakes in data-gathering or data-recording procedures. It is good practice to inspect a data set for outliers first, before other...
View Full DescriptionParameter: A Parameter is a numerical value that describes one of the characteristics of a probability distribution or population. For example, a binomial distribution is completely specified if the number of trials and probability of success are known. Here, the number of trials and the probability of success are two...
View Full DescriptionProbit: Probit is a nonlinear function of probability p: probit(p) = F-1(p) where F-1() is the function inverse to the cumulative distribution function F() of the standard normal distribution . In contrast to the probability p itsef (which takes on values from 0 to 1), the values of the...
View Full DescriptionStatistical Glossary Random Field: A random field describes an experiment with outcomes being functions of more than one continuous variable, for example U(x,y,z), where x, y, and z are coordinates in space. Random field is extension of the concept of random process into the case of multivariate argument. See also...
View Full DescriptionRandom Sampling: Random sampling is a method of selecting a sample from a population in which all the items in the population have an equal chance of being chosen in the sample. Browse Other Glossary Entries
View Full DescriptionRandom Series: A random series describes an experiment with outcomes being functions of an integer argument: U1, U2, ... (or, simply, sequences of random values - 1st value, 2nd value, etc). Random series are often referred to as "time series" and, sometimes, as random processes with discrete time. Markov chain...
View Full DescriptionRandom Variable: A random variable is a variable that takes different real values as a result of the outcomes of a random event or experiment. To put it differently, it is a real valued function defined over the elements of a sample space. There can be more than one random...
View Full DescriptionRatio Scale: A ratio scale is a measurement scale in which a certain distance along the scale means the same thing no matter where on the scale you are, and where "0" on the scale represents the absence of the thing being measured. Thus a "4" on such a scale...
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