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Seven Quality Control Tools New Quality Control Tools Project Selection Tools Advanced Quality Techniques Voice Of Customer Quality Function Deployment Failure Mode Effects Analysis Statistical Process Control Advanced Statistical Techniques Measurement Systems Analysis Hypothesis Testing Distributions Design of Experiments Control Plan Methodology Dynamic Control Plan Production Part Approval Process Service Quality Tools Healthcare Clinical Research Design Business Thesis Writting Reliability and Validity Classical Design Robust Design Multivariate Analysis Monte Carlo Simulation

5S Housekeeping Seven Quality Control Tools Design of Experiments Failure Mode Effects Analysis Just In Time Mistake Proofing Measurement System Analysis Process Flow Chart Quality Function Deployment Statistical Process Control Hypothesis Production Part Approval Process Voice of Customer Process Work Instruction Standard Operating Procedure Process Capability Control Plan Methodology Dynamic Control Plan Service Quality Total Productive Maintenance Productivity Tracking Healthcare Six Sigma Training Needs Analysis Staff Performance Evaluation Market Research Visual Management Business Thesis Writting

Thesis Awareness Thesis Outline Thesis Mindset Thesis Proposal Thesis Writing

Basic Practitioner Classical Intermediate Advance Dynamic Characteristics Problem Solving

QFD (2 days) QFD (3 days)

Basic Practitioner Intermediate Advance Online Quality Control

Service Quality Customer Complaints Customer Satisfaction Survey Managing Customer Service Delivering Customer Service Internal Customer Satisfaction Customer Loyalty Calming Upset Customers Telephone Techniques Counter Service Assertive Behavior

7 QC Tools (1 day) 7 QC Tools (2 days)

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Basic Advance

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Classical Optimization Dr. Nic

Classical Optimization Dr. Nic Design

Classical optimization software and consultancy - Classical optimization methods use a central node which has the entire responsibility or coordinating responsibility of deciding the optimal or near optimal solution to the problem.

Classical Optimization - Optimization

Classical DOE - Classical Design Of Experiments

Classical Design of Experiments shows you how to use Full Factorial experiments, Latin Square designs, Orthogonal Array designs as well as Sequential Experimentation. Each of these has unique strengths and weaknesses which can be used selectively for efficient research.

Classical Design of Experiments allows you to:

Avoid a one-factor-at-a-time experiment
Understand Latin Squares - square arrangements of factors
Understand Plackett-Burman designs
Understand Full Factorials - conducting experiments with factors and all their interactions
1. One half fractional
2. One quarter fractional
3. One sixteenth fractional
Exploit Sequential experimentation - building smaller experiments into larger experiments
1. Dealiasing 1 main and all its 2-factor interactions
2. Dealiasing all main effects
Exploit Orthogonal Arrays - arrays that provide a mutually independent factor assignment

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Classical DOE - Regression Analysis

Regression Analysis is a powerful technique of explaining relations between variables and how they affect a response. Advanced methods allow Multiple Regression Functions and Multiple Correlation analysis.

Regression Analysis allows you to:

Perform Simple linear Regression analysis including
1. linear Regression analysis
2. Correlation analysis
3. Residual analysis
4. Lack of fit and pure error analysis
Learn the matrix approach
Perform Multiple Regression analysis including
1. linear Coefficients analysis
2. Sums of squares analysis
3. Multiple Correlation analysis

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Classical DOE - Stepwise Regression Analysis

Stepwise Regression Analysis is an extension to the Regression Analysis. This method is used when there are several independent variables (x1, x2, x3,...) and one dependent variable. Two methods are available: step-up and step-down.

Stepwise Regression Analysis allows you to:

Perform a Step-up Regression
1. Conduct a correlation analysis
2. Select the highest correlated variable
3. Fit the equation
4. Iterate the procedure
Perform a Step-down Regression
1. Conduct a correlation analysis
2. Select the highest correlated variable
3. Fit the equation
4. Iterate the procedure

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Classical DOE - Orthogonal Polynomials Curve Fitting

Orthogonal Polynomial Curve Fitting is an important method of curve fitting for non linear functions. Non linear functions are usually expressed in some linear form using Taylor's theorem. Orthogonal Polynomial Curve Fitting shows how this may be done more efficiently by using orthogonal polynomial functions. Orthogonal Polynomial Curve Fitting allows you to:

Fit Orthogonal Polynomials to
1. Unifactor functions
2. Multifactor functions
3. Multifactor functions with interactions
Learn why Taylor's Expansions are not as efficient
Fit functions to complex cases

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