SylabUZ
| Course name | Design of experiments |
| Course ID | 06.9-WM-ZiIP-IJ-ANG-D-16_20 |
| Faculty | Faculty of Mechanical Engineering |
| Field of study | Management and Production Engineering |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MSc degree |
| Beginning semester | winter term 2023/2024 |
| Semester | 2 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Project | 30 | 2 | - | - | Credit with grade |
| Lecture | 30 | 2 | - | - | Exam |
Knowledge of issues related to the planning of experiments, developing the ability to analyse the results of measurements, using statistical methods of analysis.
a course in mathematical statistics
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.
Lecture: a conventional lecture
Project: a project implemented in groups or individually
| Outcome description | Outcome symbols | Methods of verification | The class form |
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
Modified by dr inż. Tomasz Belica (last modification: 12-04-2023 23:05)
