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Minitab - Engineering Statistics using Minitab


ID 2858913
Classroom 5 days 9:00-16:30
Webinar 5 days 9:00-16:30
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Data Analysts

Minitab Engineering Statistics using Minitab Training


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until one day before the training


Minitab Engineering Statistics using Minitab TrainingThis training presents a modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process. All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control.

Minitab Engineering Statistics using Minitab Training

Training Dates

  • 2021-Jan-15 - Jan-14
  • 2021-Mar-26 - Mar-25
  • 2021-Jun-04 - Jun-03

2050 EUR +VAT

Location | Enrollment


Minitab Engineering Statistics using Minitab Seminar
The Role of Statistics in Engineering

The Engineering Method and Statistical Thinking - Collecting Engineering Data - Retrospective Study - Observational Study - Designed Experiments - Random Samples - Mechanistic and Empirical Models - Observing Processes Over Time

Data Summary and Presentation

Data Summary and Display - Stem-and-Leaf Diagram - Histograms - Box Plot - Time Series Plots - Multivariate Data

Random Variables and Probability Distributions

Introduction - Random Variables - Probability - Continuous Random Variables: Probability Density Function, Cumulative Distribution Function, Mean and Variance - Important Continuous Distributions: Normal Distribution, Lognormal Distribution, Gamma Distribution, Weibull Distribution, Beta Distribution - Probability Plots: Normal Probability Plots, Other Probability Plots - Discrete Random Variables: Probability Mass Function, Cumulative Distribution Function, Mean and Variance - Binomial Distribution - Poisson Process: Poisson Distribution, Exponential Distribution - Normal Approximation to the Binomial and Poisson Distributions - More than One Random Variable and Independence: Joint Distributions, Independence - Functions of Random Variables: Linear Functions of Independent Random Variables, Linear Functions of Random Variables That Are Not Independent, Nonlinear Functions of Independent Random Variables - Random Samples, Statistics, and the Central Limit Theorem

Decision Making for a Single Sample

Statistical Inference - Point Estimation - Hypothesis Testing: Statistical Hypotheses, Testing Statistical Hypotheses, P-Values in Hypothesis Testing, One-Sided and Two-Sided Hypotheses, General Procedure for Hypothesis Testing - Inference on the Mean of a Population, Variance Known - Inference on the Mean of a Population, Variance Unknown - Inference on the Variance of a Normal Population - Inference on a Population Proportion - Other Interval Estimates for a Single Sample - Testing for Goodness of Fit

Decision Making for Two Samples

Introduction - Inference on the Means of Two Populations, Variances Known - Inference on the Means of Two Populations, Variances Unknown - The Paired t-Test - Inference on the Ratio of Variances of Two Normal Populations - Inference on Two Population Proportions - Completely Randomized Experiment and Analysis of Variance (ANOVA) - Randomized Complete Block Experiment

Building Empirical Models

Introduction to Empirical Models - Simple Linear Regression: Least Squares Estimation, Testing Hypotheses in Simple Linear Regression, Confidence Intervals in Simple Linear Regression, Prediction of a Future Observation, Checking Model Adequacy, Correlation and Regression - Multiple Regression: Estimation of Parameters in Multiple Regression, Inferences in Multiple Regression, Checking Model Adequacy - Polynomial Models - Categorical Regressors - Variable Selection Techniques

Design of Engineering Experiments

The Strategy of Experimentation - Factorial Experiments - 2k Factorial Design: 22 Design, Statistical Analysis, Residual Analysis and Model Checking, 2k Design for k 3 Factors, Single Replicate of a 2k Design - Center Points and Blocking in 2k Designs: Addition of Center Points, Blocking and Confounding - Fractional Replication of a 2k Design: One-Half Fraction of a 2k Design, Smaller Fractions (2kp Fractional Factorial Designs) - Response Surface Methods and Designs: Method of Steepest Ascent, Analysis of a Second-Order Response Surface - Factorial Experiments With More Than Two Levels

Statistical Process Control

Quality Improvement and Statistical Process Control - Introduction to Control Charts: Basic Principles, Design of a Control Chart, Rational Subgroups, Analysis of Patterns on Control Charts - R Control Charts - Control Charts For Individual Measurements - Process Capability - Attribute Control Charts: P Chart (Control Chart for Proportions) and nP Chart, U Chart (Control Chart for Average Number of Defects per Unit) and C Chart - Control Chart Performance - Measurement Systems Capability


Minitab Engineering Statistics using Minitab Trainer

Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

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- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.

Minitab Engineering Statistics using Minitab Trainer