Statistics - Engineering Statistics
Details
| ID | 2858916 |
| Duration | 5.0 days |
| Methods | Lecture with examples and exercises. |
| Prerequisites | General knowledge of math |
| Target group | Data Analysts |
Overview
This 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.
Dates
OPEN
IN-HOUSE
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Your benefits at a glance
- Flexible preferred date
- Customized content
- Intensive exchange
- High practical relevance
Comelio Media
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Services
- Lunch / catering
- Help with hotel / travel
- Comelio certificate
- Flexible: free cancellation up to one day before
Content
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 TimeData Summary and Presentation
Data Summary and Display - Stem-and-Leaf Diagram - Histograms - Box Plot - Time Series Plots - Multivariate DataRandom 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 TheoremDecision 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 FitDecision 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 ExperimentBuilding 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 TechniquesDesign 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 LevelsStatistical 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 CapabilityInstructor
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 (Comelio Medien)
978-3-939701-23-1 - System und Systematik von Fragebögen (Comelio Medien)
978-3-939701-26-2 - Oracle PL/SQL (Comelio Medien)
978-3-939701-40-8 - MS SQL Server - T-SQL Programmierung und Abfragen (Comelio Medien)
978-3-939701-69-9