41. Much like FFDs, RPDs are screening designs and can provide a linear model of the system at hand. 42. I have some data that I tried analysing using a linear model with R ( programming language ). 43. When linear models are not sufficiently accurate to represent the real process nonlinearities, several approaches can be used. 44. Generalized linear models were formulated by John Nelder and method for maximum likelihood estimation of the model parameters. 45. Fitting of linear models by least squares often, but not always, arise in the context of statistical analysis. 46. This is especially popular in the analysis of spatial data, which uses a linear model with correlated residuals. 47. In current statistical practice, probit and logit regression models are often handled as cases of the generalized linear model . 48. In the application of these distributions to linear models , the degrees of freedom parameters can take only integer values. 49. Logistic regression can be seen as a special case of the generalized linear model and thus analogous to linear regression. 50. That means the applicant has to face the fact that the linear model of moving up the ladder is gone.
