## Agenda

#####
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

## 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.

#####
Publications

- Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
- System und Systematik von Fragebögen ISBN 978-3-939701-26-2
- Oracle PL/SQL ISBN 978-3-939701-40-8
- MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

#####
Projects

- 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.

#####
Research

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