Quality Control and Improvement with MINITAB free videos and free material uploaded by IIT Bombay Staff .
Introduction of Quality.
Voice of the Customer and Kano Model.
Quality Function Deployment.
Critical to Quality Characteristics.
Data Visualization for Quality Control and Improvement.
Importance of Pareto Chart and Cause and Effect Diagram.
Design Failure Mode and Effect Analysis.
Introduction to Statistical Process Control.
X-bar and R Chart.
X-bar and S Chart.
Individual Moving Range Chart and Attribute Chart.
Attribute Control Charts and Process Capability.
Process Capability Index.
Process Performance and Sigma Level.
Process Capability for Attribute data.
Basic Statistics & Confidence Interval.
Hypothesis Testing.
One-sample t Test.
Two-sample t Test.
Paired t Test and ANOVA.
One-way ANOVA.
One-way ANOVA (Continued).
ANCOVA and Nonparametric Test.
Linear Regression.
Linear Regression(Continued) and Multiple Regression.
Best Subset Regression, Multicollinearity.
Multicollinearity, Best Subset Regression, Multiple Regression....
Design of Experiment, One-factor-at-a-time experiment.
Two-factor asymmetric Design, Symmetric Factorial Design, Two-way ANOVA.
Two-factor symmetric Design, Robust setting, Two-way ANOVA.
Measurement System Analysis.
Measurement System Analysis (Contd.).
Measurement System Analysis (Contd.), Introduction to Factorial Experiments.
Factorial Experiments.
This course will emphasize on application of different theories, tools, and techniques for Quality Control and Improvement. Most of the topics will be discussed with relevant problems and solutions in MINITAB 19 software interface.The course will emphasize two broad areas (e.g., Quality of Design and Quality of Conformance). In Quality of Design, relevant topics, such as VOC, Kano model, QFD, and FMEA, will be discussed with examples. Subsequently, the Quality of Conformance topics, such as quality control (e.g., statistical process control) and various topics related to process capability analysis, are discussed. With an objective to discuss topics related to the design of experiments, a few important statistical techniques, such as hypothesis testing, ANOVA, regression analysis, and MSA are covered in this course. Finally, various Design of Experiment (DOE) techniques for factor screening and quality improvement are elaborated with examples. These techniques include factorial designs, fractional factorial design, multiple response optimization, and the Taguchi method.
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