A well-designed experiment directly translates to high understanding of the observed measurements and its phenomenon. A well-designed controlled experiment can provide an optimum setting conditions that will minimize defects and cost and maximize the system performance.

This training program is designed for engineers, scientists, researchers and academic personnel involved in but not limited to manufacturing, assembly, research and development to provide them with essential tools and skills that will ensure a well-designed experiment. At the end of the course, the participants are also expected to be knowledgeable in utilizing different statistical software such as Minitab and Design Expert.

Price ₱20,500

Description

Duration: 5 Days
Training Fee: Php 20, 500

 

Who Should Attend:
Those involved in failure analysis, quality assurance, production, process and facilities/maintenance engineering, production line personnel, research and development and those in the academe with basic knowledge of statistics is highly encouraged

 

Rationale:
A well-designed experiment directly translates to high understanding of the observed measurements and its phenomenon. A well-designed controlled experiment can provide an optimum setting conditions that will minimize defects and cost and maximize the system performance.

This training program is designed for engineers, scientists, researchers and academic personnel involved in but not limited to manufacturing, assembly, research and development to provide them with essential tools and skills that will ensure a well-designed experiment. At the end of the course, the participants are also expected to be knowledgeable in utilizing different statistical software such as Minitab and Design Expert.

 

Course Outline:
Day 1
1. Fundamental Concepts of Statistics

1.1 Statistical Methods

1.1.1 Descriptive Statistics
1.1.2 Statistical Inference

1.2. Probability Distributions

1.2.1 Discrete Distribution
1.2.2 Continuous Distribution

1.3.Test Statistics

1.3.1 Z-test
1.3.2 T-test
1.3.3 Chi-square
1.3.4 F-test

2. Regression Analysis

2.1 Simple Linear
2.2 Multiple
2.3 Polynomial
2.4 Nonlinear

3. Correlation Analysis

3.1. Linear Regression
3.2 Multiple Regression

 

Day 2
1. Overview of Design of Controlled Experiments

1.1 Defining Research Problem
1.2 Selection of the Responses
1.3 Selection of Factors and Levels

2. Analysis of Variance

2.1 One-Factor Analyses
2.2 Two-Factor Analyses
2.3 Three-Factor Analyses

3. Experimental Designs

3.1 Randomized Blocks
3.2 Latin Squares
3.3 Split-plot and Related Designs
3.4 Incomplete Block Designs

 

Day 3
1. Variable Screening Experiments

1.1 2k Factorial Design
1.2 Fractional Factorial Design
1.3 Higher-Factorial Design

2. Taguchi Method

2.1 Two-level Orthogonal Array
2.2 Three-level Orthogonal Array
2.3 Signal/Noise Ratio

 

Day 4
1. Regression Models

1.1 Testing for Lack of Fit
1.2 Confidence Interval

2. Response Surface Methods and Process Optimization

2.1 Methods of Steepest Ascent
2.2 Central Composite Design

 

Day 5
1. Other Design and Analysis

1.1 Box-Cox Method
1.2 Unbalanced Data in Factorial Design
1.3 Analysis of Covariance

2. Statistical Software

2.1 Minitab
2.2 Design Expert
2.3 Microsoft Excel