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Hill-climbing algorithm is an algorithm which is made in use in those techniques of cluster analysis which seek to find the partition of n individuals into g clusters by optimizing some numerical index of the clustering. Since it is not possible to consider every partition of n individuals into g groups (because of the enormous number of the partitions), the algorithm starts with some given initial partition and considers individuals in turn for moving into the other clusters, creating the move if it causes an improvement in the value of the clustering index. The procedure is continued until no move of the single individual causes an improvement.
What is statistical inference? Statistical inference can be defined as the method of drawing conclusions from data which are subject to random variations. This is based o
Paired availability design is a design which can lessen selection bias in the situations where it is not possible to use random allocation of the subjects to treatments. The desig
Attitude scaling : The process of analysing the positions of the individuals on scales purporting to measure attitudes, for instance a liberal-conservative scale, ora risk-willingn
The Null Hypothesis - H0: There is no first order autocorrelation The Alternative Hypothesis - H1: There is first order autocorrelation Durbin-Watson statistic = 1.98307
Chain-binomial models : Models arising in mathematical theory of the quite infectious diseases, which postulate that at any stage in the epidemic there are a certain number of the
Glejser test is the test for the heteroscedasticity in the error terms of the regression analysis which involves regressing the absolute values of the regression residuals for the
The more effective display than a number of other methods or techniques, for instance, pie charts and bar charts, for displaying the quantitative data which are labeled. An instanc
Introduction to Generalized Linear Models (GLM) We introduce the notion of GLM as an extension of the traditional normal-theory-based linear regression models. This will be very
PRINCIPLES OF MODELLING IN OR.
Generalized method of moments (gmm) is the estimation method popular in econometrics which generalizes the method of the moments estimator. Essentially same as what is known as the
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