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Stephen L. Nelson (born 1959) is the author of more than 160 books about using personal computers, including Quicken for Dummies, QuickBooks for Dummies, MBA's Guide to Microsoft Excel, and Excel Data Analysis for Dummies.
MCA is performed by applying the CA algorithm to either an indicator matrix (also called complete disjunctive table – CDT) or a Burt table formed from these variables. [citation needed] An indicator matrix is an individuals × variables matrix, where the rows represent individuals and the columns are dummy variables representing categories of the variables. [1]
Equation-free modeling is a method for multiscale computation and computer-aided analysis.It is designed for a class of complicated systems in which one observes evolution at a macroscopic, coarse scale of interest, while accurate models are only given at a finely detailed, microscopic, level of description.
Dummy variables are useful in various cases. For example, in econometric time series analysis, dummy variables may be used to indicate the occurrence of wars, or major strikes. It could thus be thought of as a Boolean, i.e., a truth value represented as the numerical value 0 or 1 (as is sometimes done in computer programming).
The multitaper method overcomes some of the limitations of non-parametric Fourier analysis.When applying the Fourier transform to extract spectral information from a signal, we assume that each Fourier coefficient is a reliable representation of the amplitude and relative phase of the corresponding component frequency.
The general ARMA model was described in the 1951 thesis of Peter Whittle, who used mathematical analysis (Laurent series and Fourier analysis) and statistical inference. [ 12 ] [ 13 ] ARMA models were popularized by a 1970 book by George E. P. Box and Jenkins, who expounded an iterative ( Box–Jenkins ) method for choosing and estimating them.
Created Date: 8/30/2012 4:52:52 PM
= the number of data points in , the number of observations, or equivalently, the sample size; k {\displaystyle k} = the number of parameters estimated by the model. For example, in multiple linear regression , the estimated parameters are the intercept, the q {\displaystyle q} slope parameters, and the constant variance of the errors; thus, k ...