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Lecture 19: Two-sample t Test
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Classroom Contents
Quality Control and Improvement with MINITAB
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- 1 Course Introduction - Quality Control and Improvement with MINITAB.
- 2 Lecture 1: Introduction of Quality
- 3 Lecture 2: Voice of the Customer and Kano Model
- 4 Lecture 3: Quality Function Deployment
- 5 Lecture 4: Critical to Quality Characteristics
- 6 Lecture 5 : Data Visualization for Quality Control and Improvement
- 7 Lecture 6: Importance of Pareto Chart and Cause and Effect Diagram
- 8 Lecture 7: Design Failure Mode and Effect Analysis
- 9 Lecture 8: Introduction to Statistical Process Control
- 10 Lecture 9: X-bar and R Chart
- 11 Lecture 10: X-bar and S Chart
- 12 Lecture 11: Individual Moving Range Chart and Attribute Chart
- 13 Lecture 12: Attribute Control Charts and Process Capability
- 14 Lecture 13: Process Capability Index
- 15 Lecture 14: Process Performance and Sigma Level
- 16 Lecture 15: Process Capability for Attribute data
- 17 Lecture 16: Basic Statistics & Confidence Interval
- 18 Lecture 17: Hypothesis Testing
- 19 Lecture 18: One-sample t Test
- 20 Lecture 19: Two-sample t Test
- 21 Lecture 20: Paired t Test and ANOVA
- 22 Lecture 21: One-way ANOVA
- 23 Lecture 22: One-way ANOVA (Continued)
- 24 Lecture 23: ANCOVA and Nonparametric Test
- 25 Lecture 24: Linear Regression
- 26 Lecture 25: Linear Regression(Continued) and Multiple Regression
- 27 Lecture 26:Best Subset Regression, Multicollinearity
- 28 Lecture 27: Multicollinearity, Best Subset Regression, Multiple Regression...
- 29 Lecture 28: Design of Experiment, One-factor-at-a-time experiment
- 30 Lecture 29: Two-factor asymmetric Design, Symmetric Factorial Design, Two-way ANOVA
- 31 Lecture 30: Two-factor symmetric Design, Robust setting, Two-way ANOVA
- 32 Lecture 31: Measurement System Analysis
- 33 Lecture 32: Measurement System Analysis (Contd.)
- 34 Lecture 33: Measurement System Analysis (Contd.), Introduction to Factorial Experiments
- 35 Lecture 34: Factorial Experiments
- 36 Lecture 35: Factorial Experiments (Contd.)
- 37 Lecture 36: Factorial Experiments (Contd.)
- 38 Lecture 37: Blocking in Factorial Design.
- 39 Lecture 38: Multiple response Optimization & RSM
- 40 Lecture 39: Fractional Factorial Design
- 41 Lecture 40: Taguchi Method
