"Two separate logistic regression analyses were conducted for low- and high-altitude sectors to determine whether a set of dynamic sector characteristics variables could reliably discriminate between operational error (OE) and routine operation (RO) traffic samples. OE data were derived from SATORI re-creations of OEs occurring at the Indianapolis Air Route Traffic Control Center between 9/17/2001 and 12/10/2003. RO data were extracted from System Analysis Recordings (SARs) taped between 5/8/2003 and 5/10/2003"--Report documentation page.

Logistic regression analysis of operational errors and routine operations using sector characteristics /

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fishe

This study examined prediction and classification of operational errors (OEs) and routine operations (ROs) using sector characteristics variables. Average Control Duration, Aircraft Mix Index, Average Lateral Distance, Average Vertical Distance, Number of Handoffs, Number of Point Outs, Number of Transitioning Aircraft, and Number of Heading Changes were used as predictors in two stepwise logistic regression analyses conducted for the high-altitude and low-altitude sectors. In the high-altitude sample, variables included in the final model (Number of Heading Changes, Number of Transitioning Aircraft, and Average Control Duration) accurately classified OE and RO samples for 80% of the cases. In the low-altitude sample, variables included in the final model (Number of Point Outs, the Number of Handoffs, and the Number of Heading Changes) accurately classified OE and RO samples for 79% of the cases. Although logistic regression cannot be used to determine causation, it effectively identified variables that predicted the occurrence of OEs.

Business innovation and industrial intelligence are paving the way for a future in which smart factories, intelligent machines, networked processes and Big Data are combined to foster industrial growth. The maturity and growth of instrumentation, monitoring and automation as key technology drivers support Industry 4.0 as a viable, competent and actionable business model. This book offers a primer, helping readers understand this paradigm shift from industry 1.0 to industry 4.0. The focus is on grasping the necessary pre-conditions, development & technological aspects that conceptually describe this transformation, along with the practices, models and real-time experience needed to achieve sustainable smart manufacturing technologies. The primary goal is to address significant questions of what, how and why in this context, such as:What is Industry 4.0?What is the current status of its implementation?What are the pillars of Industry 4.0?How can Industry 4.0 be effectively implemented?How are firms exploiting the Internet of Things (IoT), Big Data and other emerging technologies to improve their production and services?How can the implementation of Industry 4.0 be accelerated?How is Industry 4.0 changing the workplace landscape?Why is this melding of the virtual and physical world needed for smart production engineering environments?Why is smart production a game-changing new form of product design and manufacturing?