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Cooper Fulleon 600124FULL-0014 Remote Indicator DAGS 3000 Buzzer - Red Lens - White Housing

Cooper Fulleon 600124FULL-0014 Remote Indicator DAGS 3000 Buzzer - Red Lens - White Housing

Cooper Fulleon 600124FULL-0014 Remote Indicator DAGS 3000 Buzzer - Red Lens - White HousingUsed as a supplemental alert to the fire alarm the Fulleon LED remote indicator has been developed to provide discreet remote indication.Versatile and dependable it can be used with the majority of fire alarm system point detectors as a pre-alarm or to indicate a detector in alarm which is positioned in a void or obscure location.A flexible design allows sound and visual alert to be independently controlled. With instilled piezo electric technology energy efficiency is a given whilst the option of adding a buzzer helps you tailor your device to suit the environment.Key FeaturesLow current ideal when both and audible and visual indicator is requiredUnobtrusive blends in with modern building Design suitable for a wide range of applicationsSlim-line DesignWide operating voltage

GBP 16.12
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Statistical Rethinking A Bayesian Course with Examples in R and STAN

Statistical Rethinking A Bayesian Course with Examples in R and STAN

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error missing data and Gaussian process models for spatial and phylogenetic confounding. The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions splines ordered categorical predictors social relations models cross-validation importance sampling instrumental variables and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling showing how domain-specific scientific models can be built into statistical analyses. Features Integrates working code into the main text Illustrates concepts through worked data analysis examples Emphasizes understanding assumptions and how assumptions are reflected in code Offers more detailed explanations of the mathematics in optional sections Presents examples of using the dagitty R package to analyze causal graphs Provides the rethinking R package on the author's website and on GitHub | Statistical Rethinking A Bayesian Course with Examples in R and STAN

GBP 74.99
1