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the exponential logarithm function as an algorithm for
the exponential logarithm function as an algorithm for

... sophistication desired. As Goosen, Gold and Pray, and others have pointed out, careful attention must be paid to the limits put on each variable to avoid one single factor having an unrealistic effect on the final functional value. ...
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... force of the hammer. Also, provided the stake is driven into soft ground with little resistance, the impulse of the ground’s reaction on the stake can also be considered negligible or non-impulsive. ...
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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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