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Training on
Uncover Design of Experiments
Understanding variation to
experimental design process

Universiti Malaya, KL
03 & 04 APR, 2019
Training on
Multivariate Data Analysis
Uncover Statistical Techiques for
Analyzing Data with Many Variables

Universiti Malaya, KL
01 & 02 APR, 2019

" understand applicability and concept of DOE in details
followed by analysis and interpretation of results
in a simple and understandable manner ...

interpret complex data quickly and confidently using
tools of chemometrics and multivariate analytical methods..."

Data analysis is a vital part of science today and in assessing quality, modern data sets contain many variables where the most interesting trends are hidden within a combination of the variables. Using the lastest multivariate techniques with design of experiments, participants will learn how to interpret complex data quickly and confidently. You will discover the secret of overviewing data tables and also learn how to build robust predictive models that translate data into decisions.

Scientists and Engineers no longer can afford to experiments in a trial-and-error manner, changing one factor at a time, the way Edison have done in developing the light bulb. A far more effective method is to apply a computer-enhanced, systematic approach to experimentation, one that considers all factor simultaneously. This approach is known as Design of Experiments (DoE), and corporations across the globe are adopting it as a cost effective way to solve serious problems afflicting to their operations. DoE provides information about the interaction of factors and the way the total system works, something not obtainable through testing one factor at a time while holding other factor constant. Another advantage of DoE is that it shows how interconnected factors respond over a wide range of values, without requiring the testing of all possible values directly.

Multivariate analysis is used widely in many industries, from raw material analysis and drug discovery in the pharmaceutical industry, early event detection and gasoline blending in refineries, right through to predicting future market trends in business intelligence applications. It can be used for measuring data sets with many input variables or for investigating the trends in time series data, all of which provide a better understanding of a given issue and often result in resource and time savings. It can generates data models that can be used for on-line prediction and classification and it also generates data models for faster product and process optimization for application in Spectroscopy, Chemometrics, Sensometrics, Process Analytical Technology, Product Development and Quality Control.

DoE Course Benefits

This 2 day course intends to meet the needs of professionals, engineers and researcher, begins with the fundamentals of Design of Experiments (DoE) methods and continues with concepts, principles and requirements. Topics include full factorial designs, fractional factorial designs, screening designs, the response surface methodology and reliability DOE., with intensive, hands-on training using latest software tools.

Carefully selected course exercises and application examples provide participants with Theory-to-Skills Building knowledge transfer, enabling them to achieve proficiency in applications development.

Highly interactive workshop with practical examples and exercises to understand applicability and concept of DOE in details followed by analysis and interpretation of results in a simple and understandable manner. Statistical software Design Expert will be used during the workshop



MVA Course Benefits

For most real-world problems we need to use multivariate analysis to model the relationship to the response. We need tools to mine our data, and understand the relationships in them, whether for pattern recognition, or to develop models that can be used to predict values for new samples. Multivariate analysis (MVA) has wide application to data including instrumental data, medical diagnostics, census data, economic data, marketing data, or even a sports team’s performance. MVA gives us a means to find the relationships in the data, and provides tools to visualize the relationships between samples and variables. It can be used for both qualitative and quantitative analysis.

This 2-days hands-on training you will learn how to interpret complex data quickly and confidently using latest software tools of chemometrics and multivariate analytical methods, participants can quickly and safety start using the methods in their own work to optimize, classify or predict products and processes. Solutions 4U provides CAMO's Unscrambler® Software for the exercise session during the course.


DOE Course Outline

  • Basic Statistical Concepts
    • Concept of variation
    • Normal distribution & probability using standard normal curve
    • Hypothesis testing concepts
    • Basic statistical test
      (z-test, t-test, 2-t-test & ANOVA)
  • Design of Experiments
    • The Experimental Design Process
    • Screening and Optimization designs.
    • Terminologies of DoE
    • Basic Principles of DoE
    • Selection of appropriate designs
  • Factorial Designs
    • Full Factorial designs at two level (2K)
    • Fractional Factorial Design (2K-P)
    • Phenomenon of Confounding
    • Understanding of Centre-Point, Blocking & Randomization
    • Analyzing of DoE Results
      (Significant Factors, Main Effect, Interacton Effects, Residuals)
    • Prediction Equation and Optimizing Response
  • Plackett Burman Screening Design
  • Response Surface Designs
    • Central Composite Designs
    • Box Behnken Designs
  • Hands-on Exercises
  • Summary and Conclusion

MVA Course Outline

  • Introduction to MVA
  • Multivariate Data
  • Data Analysis & chemometrics in practice
  • Multivariate Analysis workflow
  • Uni-variate data, Applied Statistics and Plotting
  • Introduction to Multi-variate Analysis
  • Data Collection
  • Data Checking and Pre-processing
  • Principal Component Analysis (PCA)
    • Principles Theory of PCA
    • Model rank and cross validation
    • Score Plot, Loading Plot & Bi-Plots
  • Detecting Outliners in PCA
  • Importance of validation in MVA
  • SIMCA Classification
  • Multivariate Regression Methologies:
    • MLR- Multi Linear Regression
    • PCR - Principal Component Regression
    • PLS - Partial Least Squares
  • Interpreting a regression model in 5 steps
  • Prediction Process
  • Detecting and dealing with outliers
  • Study impact in regression
  • Hands-on exercise


Who Should Attend
The course combines theoretical studies and practical workshops, ensuring that each participant gets individual focus, and understands the practical uses of MVA & DOE application. It is intended for researchers, scientists, chemists and engineers involved in process understanding, rapid quality monitoring, product formulation, metabolomics and sensory science or likely to work with spectroscopic instruments (NIR, FTIR, UV, UV/VIS, NMR, Raman, Mass Spectroscopy), chromatography instruments (LC, GC) and other sources of multivariate data as part of laboratory, R&D, quality control or production processes. No prior knowledge of statistics is assumed.

Math skills, knowledge of basic statistics. No prior knowledge of Design-Expert is required to attend this program for the DoE Training and Unscrambler for the MVA Training.

Note: Due to the nature of the course and the learning expectations, the availability seats are limited. You need to register early to obtain confirmation of your space.

03 & 04 APR 2019
Training on Design of Experiments
01 & 02 APR 2019
Training on Multivariate Data Analysis
Venue : Universiti Malaya, Kuala Lumpur
10 % Early Registration Discount before 01 MAR 2019
10 % Group Discount for 3 or more from same organization

Other upcoming courses to enrich and enhance your technical computing capability and data anaysls skill-set, Just click onto the Title to find out more details.

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