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Generalized principal components analysis: The non-linear version of the principal components analysis in which the goal is to determine the non-linear coordinate system which is most in agreement with the data configuration. For instance, for the bivariate data, y1,y2, if the quadratic coordinate system is sought, a variable z is defined as given below: with the coefficients being set up so that the variance of z is a maximum amongst all such quadratic functions of y1 and y2.
Banach's match-box problem : The person carries two boxes of matches, one in his left and one in his right pocket. At first they comprise N number of matches each. When the person
I do have a data of real gdp for each state and from 2000 to 2010 and I also have estimated population of illigel immigrants for each state from 2000 to 2010. In my thesis I am try
The probability distribution which is a linear function of the number of component probability distributions. This type of distributions is used to model the populations thought to
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Bootstrap : The data-based simulation method/technique for the statistical inference which can be used to study the variability of the estimated characteristics of the probability
Harris and Stevens forecasting is the method of making short term forecasts in the time series which is subject to abrupt changes in pattern and the transient effects. Instances o
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2 >
A law supposedly applicable to voting behaviour which has a history of several decades. It may be stated thus: Consider a two-party system and suppose that the representatives of t
The Null Hypothesis - H0: There is autocorrelation The Alternative Hypothesis - H1: There is no autocorrelation Rejection Criteria: Reject H0 (n-s)R 2 > = (1515 - 4) x (0.
This is the theorem which states that if the error terms in a multiple regression have the same variance and are not corrected, then the estimators of the parameters in the model p
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