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JMP - Modeling Process Cycles (JIMPC)
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This one day course teaches you how to identify and model continuous process data. Students will participate in interactive demonstrations of JMP software in realistic scenarios. Topics include Autoregressive Integrated Moving Average (ARIMA) nonseasonal and seasonal models, spectral analysis, and multiple regression models using sine and cosine functions or categorical dummy variables.

The course focuses on identifying the magnitude and periods of cycles in process data and using different methods for modeling the cycles.

Content:

  • Modelling Stationary Time Series
  • ARIMA Models
  • Modeling Cycles with Regression Models
  • Using spectral analysis
  • Modeling cycles with sine and cosine functions
  • Modeling cycles with dummy variables
  • Seasonal ARIMA Models

Requirements:

Students should have some basic experience in how to work with JMP (Explorative Data Analysis with JMP). Knowledge in linear regression analysis - like provided in the course ANOVA and Regression with JMP - is useful but not required.