ABB module card 216NG63A HESG441635R1 HESG216877K

ABB module card 216NG63A HESG441635R1 HESG216877K

Technical data of product parameters

A high degree of correlation among the independent variables makes it very difficult to come up with a
reliable estimate of the effects of particular variables. For example, if horizontal curvature is correlated
with the clear zone/roadside hazards, then it may be difficult to isolate the safety effect of horizontal
curvature. There are no easy solutions to this problem. It may be tempting to remove one of the216NG63A HESG441635R1 HESG216877K
correlated variables, but this may lead to omitted variable bias (discussed above). Some statistical
routines include tools to assess the extent of this problem. For example, one could examine the
correlation matrix of the estimated parameters which will provide information about the correlation
between pairs of variables. If collinearity is a concern, then the full or hierarchical Bayes method can
help to reduce the issue by distinguishing heterogeneity from “noise” in the data216NG63A HESG441635R1 HESG216877K
Overfitting of prediction models
Overfitting of prediction models can occur when some of the relationships that appear to be statistically
significant is just “noise”. In other words, the model does a poor job of showing the underlying
relationship. Typically, overfitting usually occurs when the model is too complex and includes too many
parameters. This results in models that do not predict crashes very well and also increases the chances
of correlation between the different variables in the model. One way to address this problem is using
cross-validation. In cross-validation the data set is randomly divided into a two parts, where one part is216NG63A HESG441635R1 HESG216877K
used for estimating the model and the other part is used for validation. Examples of validation can be
seen in two studies led by Simon Washington (Washington et al., 2001; Washington et al., 2005).
Another approach is to use relative goodness of fit measures such as the Akaike Information Criterion216NG63A HESG441635R1 HESG216877K
(AIC) and Bayesian Information Criterion (BIC) that penalize models with more estimated parameters.

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Introduction to main products

Focus on DCS, PLC, robot control system and large servo system.

Main products: various modules/cards, controllers, touch screens, servo drives.

Advantages: supply imported original products, professional discontinued parts, fast delivery, accurate delivery time,

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