DS200AAHAH1A industrial control gas turbine equipment GE Electric

DS200AAHAH1A industrial control gas turbine equipment GE Electric

Technical data of product parameters

A high degree of correlation among the independent variables makes it very difficult to come up with aDS200AAHAH1A
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 the
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 theDS200AAHAH1A
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 data (Orme, 2000).
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 manyDS200AAHAH1A
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 isDS200AAHAH1A
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 Criterion
(AIC) and Bayesian Information Criterion (BIC) that penalize models with more estimated parameters.
Low sample mean and small sample size
Since crashes are fortunately rare events, it is not uncommon to find situations where some roadway
sections or intersections may have very few crashes

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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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