Course details

Statistics for the Engineering

Teaching: Completely taught in English

ECTS: 5

Level: Undergraduate

Semester: Summer

Prerequisites:

Load:

Lectures	Exercises	Laboratory exercises	Project laboratory	Physical education excercises	Field exercises	Seminar	Design exercises	Practicum
30		24	0	0	0	0	0	0

Course objectives:

Basic knowledge of statistic methods used in industrial engineering, quality management, theory of reliability, simulation methods and models.

Student responsibilities:

Attending lectures and exercises.

Grading and evaluation of student work over the course of instruction and at a final exam:

Partial exams or final exam 100%

Upon successful completion of the course, students will be able to (learning outcomes):

1 . Classify problems for statistical analysis.

2 . Identify the concepts that are prerequisites for solving the problem of complex statistical methods.

3 . Select the appropriate data for statistical analysis.

4 . Apply statistical methods.

5 . Apply acquired knowledge when using computer software in data analysis.

6 . Interpret the results.

Lectures

1. Introduction: The role of statistics in the field of engineering. Probability and combinatorics - models and applications in real systems.

2. Descriptive statistics - statistical processing of empirical data. Parameters of statistical sets (position and scatter). Measuring scales. Features.

3. Random variables - discrete and continuous random variable relation with real problems in engineering.

4. Discrete variable distributions in engineering problems: hypergeometric, binomial, Poisson distribution.

5. Distributions of continuous random variables with application to engineering problems: Normal, Weibull, Erlang. Gamma and beta distribution. Student's - t, χ2, F-distribution.

6. Introduction to inferential statistics - Fundamentals of sampling theory: random sample, sample distribution. Definition of the standard error.

7. Confidence interval calculation of the population mean and variance.

8. Statistical test of hypotheses. Types of errors and level of significance. Hypothesis tests about population mean, one sample test, two sample test.

9. Hypothesis test for proportion, hypothesis test on variance.

10. Fitting theoretical distributions to empirical data. Estimation of selected distribution. Tests: chi-square, Kolmogorov-Smirnov, probability paper.

11. Analysis of variance: theoretical base, decomposition of sum of square.

12. 2-way analysis of variance. Latin square, Greco-Latin square models. Factorial ANOVA.

13. Use of correlation and regression analysis in engineering. Sample correlation coefficient, distribution of sample correlation coefficient.

14. Types of regression analysis: linear, nonlinear; single, multiple.

15. Introduction to design of experiment. Role in product and process optimization. "OFAT" experiments and factorial experimental plans. Examples of use of DOE in product and process optimization - examples from practice.

Exercises

1. Examples of application.

2. Calculation of basic statistical parameters - examples. Graphical display of data.

3. Introduction to statistical software.

4. Calculation of parameters of distributions. Displaying distributions and calculating outcome probabilities - examples.

5. Calculation of parameters of distributions. Displaying distributions and calculating outcome probabilities. Application in engineering - examples.

6. Sampling procedure examples, calculations of sample parameters.

7. Calculation of confidence intervals using real engineering examples.

8. Preparation for preliminary exam (e-learning) Preliminary exam 1

9. Confidence interval estimates and testing of statistical hypotheses - examples.

10. Modelling the process using theoretical distributions - examples from practice.

11. ANOVA 1-way examples.

12. ANOVA 2-way examples.

13. Correlation and regression analysis - examples.

14. Modelling of causal relationships using regression analysis - examples.

15. Preparation for preliminary exam (e-learning) Preliminary exam 2

Compulsory literature:

1. Design and analysis of Experiments, Douglas C. Montgomery, , 2012, p. 0-0

3. Applied Statistics and Probability for Engineers, Douglas C. Montgomery, George C. Runger, J. Wiley&Sons , 2003, p. 0-0

Recommended literature:

4. Response Surface Methodology, Raymond H. Myers, Douglas C. Montgomery, 2000, 2010, p. 0-0

5. Statistics for the engineering and computer sciences, Mendenhall W., Sincich T., 1992, 1992, p. 0-0