SylabUZ

# Design of experiments - opis przedmiotu

Informacje ogólne
 Nazwa przedmiotu Design of experiments Kod przedmiotu 06.9-WM-ZiIP-IJ-ANG-D-16_20 Wydział Wydział Mechaniczny Kierunek Management and Production Engineering Profil ogólnoakademicki Rodzaj studiów drugiego stopnia z tyt. magistra inżyniera Semestr rozpoczęcia semestr zimowy 2022/2023
Informacje o przedmiocie
 Semestr 2 Liczba punktów ECTS do zdobycia 5 Typ przedmiotu obowiązkowy Język nauczania angielski Sylabus opracował dr inż. Iwona Pająk
Formy zajęć
 Forma zajęć Liczba godzin w semestrze (stacjonarne) Liczba godzin w tygodniu (stacjonarne) Liczba godzin w semestrze (niestacjonarne) Liczba godzin w tygodniu (niestacjonarne) Forma zaliczenia Projekt 30 2 - - Zaliczenie na ocenę Wykład 30 2 - - Egzamin

## Cel przedmiotu

Knowledge of issues related to the planning of experiments, developing the ability to analyse the results of measurements, using statistical methods of analysis.

## Wymagania wstępne

a course in mathematical statistics

## Zakres tematyczny

Lecture:

L1-2: Introduction. Basic concepts: scientific research, experimental research, theory of experiment, experience, active and passive experiment. Classification of experimental plans. Theoretical basis of experimental research. Problem identification and formulation. Choice of response variables. Selection of factors to be varied. Establishing a research method: choice of experimental design, determining the number of replicates. Statistical analysis of the data.

L3-4: Review of problems in mathematical statistics. Statistical distributions and their parameters. Point estimation: measures of central tendency, measures of spread/dispersion and measures of distortion. Data analysis through graphical presentation (histogram, box plot, quantile-quantile plot, normality plot, scatter plot). Hyphotesis testing: inference about the differences in means and goodness-of-fit tests.

L5: Simple comparative experiments. Analysis of the experiments results using of two-Sample t-Test. Checking assumptions: graphical analysis as well as tests for equality of the variances of two populations and goodness-of-fit tests.

L6: Power analysis and calculation minimum sample size. Standarized effect and effect. Determining sample size (umber of replicates).

L7-8: Experiments with a single factor. One-way ANOVA. Checking assumptions. Post-hoc tests. Effect size and measures. Selected measures of the effect. Determining sample size.

L9-10: Experiments with two factors. Factorial designs: main effects and interactions. Two-way ANOVA. Checking assumptions. Post-hoc tests. Determining sample size.

L11: Two and multidimensional random variables.  Measures of dependence among random variables.

l12-13: Regression analysis. Linear Regression. Least squares method. Regression equation quality indicators. Hypothesis testing in multiple regression: test for significance of regression, tests on individual regression coefficients. Checking assumptions - residual analysis.

L14: Stepwise regression. Single factor experiments: relationships between analysis of variance and analysis of regression.

L15: Experimental designs. Full and fractional factorial designs:  two-level, three-level and multi-level.

Project:

P1-P3: Carrying out a simple comparative experiment. Analysis of experiment results. Checking assumptions. Power analysis. Checking the sample size for the desired effect.

P4-P6: Conducting a two-factor experiment. Analysis of experiment results. Checking assumptions. Power analysis.  Checking the sample size for the desired effect..

P7-P9: Conducting an experiment to develop a model of the selected process. Analysis of experiment results. Checking assumptions.

P10-P12: Presentation of the results.

P13-P14: Verification of solutions - passing the projects.

## Metody kształcenia

Lecture: a conventional lecture

Project: a project implemented in groups or individually

## Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

 Opis efektu Symbole efektów Metody weryfikacji Forma zajęć

## Warunki zaliczenia

Lecture: written exam preceded by obtaining a credit for project classes

Project: arithmetic mean of the grades obtained for each project activity

Final grade: the condition for passing the course is to pass all its forms, the final grade for the course is the arithmetic mean of the grades for individual forms of classes

## Literatura podstawowa

1. Montgomery D. C., Design and Analysis of Experiments, Wiley, 2012
2. Dean A., Voss D., Draguljić D., Design and Analysis of Experiments, Springer, 2017
3. Hines W. W., Montgomery D. C., Goldsman D. M., Borror C. M., Probability and Statistics in Engineering, Wiley, 2003

## Literatura uzupełniająca

1. Toutenburg H., Statistical Analysis of Designed Experiments, Springer, 2002
2. Davim J. P., Design of Experiments in Production Engineering, Springer 2016
3. Wild C. J., Seber G. A. F. , Chance Encounters: A First Course in Data Analysis and Inference, Wiley, 1999

## Uwagi

Zmodyfikowane przez dr inż. Tomasz Belica (ostatnia modyfikacja: 25-04-2022 09:38)