Design of Experiments and Statistical Process Control Help

Design of Experiments and Statistical Process Control Help

Statistical Process Control is a method of quality control where in statistical tools are used to control error rates. In case of Statistical Process Control, quality control is implemented to monitor and control a process. It helps reduce error rates and makes the process efficient. Statistical Process Control and design of experiments are widely used in business operations, manufacturing processes and other applications in production.

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Following is the comprehensive list of topics in which we offer the quality solutions:

  • General block design and its information matrix
  • Criteria for connectedness
  • Balance and orthogonality
  • Intrablock analysis
  • Estimability
  • Balance incomplete block design (BIBD)
  • Analysis of Covariance (ANCOVA)
  • Completely Randomized Design (CRD)
  • Randomized Block Design (RBD)
  • Latin Square Design (LSD)
  • Recovery of interblock information
  • Youden design-intrablock analysis
  • Analysis of covariance in a general Gauss-Markov model
  • Fixed, mixed and random effects models
  • Variance components
  • Test for variance components
  • Missing plot technique
  • General theory and applications
  • General factorial experiments
  • Factorial effects
  • Best estimates and testing the significance of factorial effects
  • F 2 and 3 factorial experiments in randomized blocks
  • Complete and partial confounding
  • Fractional replication for symmetric factorials
  • Split plot and split block experiments
  • Parametric relations
  • Intra block analysis of bib design
  • Youden squares design
  • Intra block design analysis of yauden square design
  • Lattice design
  • Simple and balanced lattice design
  • Analysis of lattice design
  • Missing plot techniques
  • Factorial experiment
  • Main effects and interaction effects
  • 2^n and 3^n factorial experiment
  • Analysis of 2^n and 3^n factorial experiments in randomized block.
  • Confounding experiments
  • Complete partial and balanced confounding and its anova table.
  • Split and strip plot designs
  • Analysis of covariance in a general grass-markov model
  • Response surface designs
  • First order and second order response surface designs
  • Second order rotable designs
  • Application areas
  • Response surface experiments
  • First order designs and orthogonal designs
  • Clinical trials
  • Longitudinal data
  • Treatment-control designs
  • Model validation and use of transformation
  • Tukey’s test for additivity
  • Full factorial
  • Response surface (central composite and Box-Behnken)
  • Fractional factorial
  • D-optimal
  • Latin hypercube
  • Gage repeatability and reproducibility studies
  • Estimation of process capability
  • Control charts
  • Western Electric and Nelson control rules to control chart data

Statistical process ctrl