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... suppose Ax1 = Ax2 = b and x1! x2 then A(x1- x2) = Ax1 - Ax2 = b - b = 0 and for any scalar k we have A[x1- k(x1-x2)] = Ax1- k A(x1-x2) = b - k 0 = b so x1- k(x1-x2) is a solution of Ax = b for any scalar k as long as there are an infinite number of scalars [e.g. for a real vector space] there will b ...
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... periods, during the same time period on successive days Be aware of data censoring: the quantity is not observed in its entirety, danger of leaving out long process times Check for relationship between variables, e.g. build scatter diagram Check for autocorrelation Collect input data, not performanc ...
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Generalized linear model

In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression. They proposed an iteratively reweighted least squares method for maximum likelihood estimation of the model parameters. Maximum-likelihood estimation remains popular and is the default method on many statistical computing packages. Other approaches, including Bayesian approaches and least squares fits to variance stabilized responses, have been developed.
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