Unformatted text preview: lts can be obtained with relatively small data sets.
Finally, the theory associated with linear regression is well understood and allows for construction of different types of easily interpretable statistical intervals for predictions,
calibrations, and optimizations. These statistical intervals can then be used to give clear answers to scientific and engineering questions.
Disadv ant ages: The main disadvantages of linear least squares are limitations in the shapes that linear models can assume over long ranges, possibly poor extrapolation
properties, and sensitivity to outliers. Linear models with nonlinear terms in the predictor variables curve relatively slowly, so for inherently nonlinear processes it becomes
increasingly difficult to find a linear model that fits the data well as the range of the data increases. As the explanatory variables become extreme, the output of the linear
model will also always more extreme. This means that linear models may not be effective for extrapolating the results of a process for which data cannot be collected in the
region of interest. Of course extrapolation is potentially dangerous regardless of the model type. Finally, while the method of least squares often gives optimal estimates of the
unknown parameters, it is very sensitive to the presence of unusual data points in the data used to fit a model. One or two outliers can sometimes seriously skew the results
of a least squares analysis. This makes model validation, especially with respect to outliers, critical to obtaining sound answers to the questions motivating the construction of
the model.
us e ful link:[13] (http://www.uco.es/dptos/prod animal/p animales/cerdo iberico/Bibliografia/p253.pdf) [14] (http://www.cs.au.dk/~cstorm/courses/ML/slides/linearregression and classification.pdf) Logistic Regression October 16, 2009
The logistic regression (http://en.wikipedia.org/wiki/Logistic_regression) model arises from the desire to model the posterior probabilities of the
classes via l...
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