|
|
haiyan851117(金币+40, 博学EPI+1): 2010-06-25 00:36:20
1. In regression analysis, a dummy variable (also known as indicator variable or just dummy) is one that takes the values 0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift the outcome.
2. Least squares corresponds to the maximum likelihood criterion if the experimental errors have a normal distribution and can also be derived as a method of moments estimator. In a linear model, if the errors belong to a Normal distribution the least squares estimators are also the maximum likelihood estimators.
3. Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical variable at a time. In probability theory, a stochastic process, or sometimes random process, is the counterpart to a deterministic process (or deterministic system). Instead of dealing with only one possible reality of how the process might evolve under time (as is the case, for example, for solutions of an ordinary differential equation), in a stochastic or random process there is some indeterminacy in its future evolution described by probability distributions.
4. In statistics, the interclass correlation (or interclass correlation coefficient) measures a bivariate relation among variables. The Pearson correlation coefficient is the most commonly used interclass correlation. Linear discriminant analysis (LDA) and the related Fisher's linear discriminant are methods used in statistics and machine learning to find a linear combination of features which characterize or separate two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification. |
|
回复此楼
言之凿凿，8月21号(明天)放榜
+12